{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:42:46.234281Z",
     "start_time": "2018-11-24T10:42:46.219888Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import requests\n",
    "import csv\n",
    "from bs4 import BeautifulSoup\n",
    "from datetime import datetime, timedelta\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa import stattools\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "import os\n",
    "from pandas import Series\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from math import sqrt\n",
    "from matplotlib import pyplot\n",
    "from numpy import array\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "os.environ['CUDA_VISIBLE_DEVICES']=''\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (15, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:22:28.748764Z",
     "start_time": "2018-11-23T13:22:28.740120Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def get_load_data(date):\n",
    "    url = 'http://www.delhisldc.org/Loaddata.aspx?mode='\n",
    "    print('Scraping ' + date, end=' ')\n",
    "    resp = requests.get(url + date) # send a get request to the url, get response\n",
    "    soup = BeautifulSoup(resp.text, 'lxml') # Yummy HTML soup\n",
    "    table = soup.find('table', {'id':'ContentPlaceHolder3_DGGridAv'}) # get the table from html\n",
    "    trs = table.findAll('tr') # extract all rows of the table\n",
    "    if len(trs[1:])==288: # no need to create csv file, if there's no data\n",
    "        with open('monthdata.csv', 'a') as f:  #'a' makes sure the values are appended at the end of the already existing file\n",
    "            writer = csv.writer(f)\n",
    "            for tr in trs[1:]:\n",
    "                time, delhi = tr.findChildren('font')[:2]\n",
    "                writer.writerow([date + ' ' + time.text, delhi.text])\n",
    "    if len(trs[1:]) != 288:\n",
    "        print('Some of the load values are missing..')\n",
    "    else:\n",
    "        print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:23:33.444840Z",
     "start_time": "2018-11-23T13:23:10.081611Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "for i in range(31, 0, -1):\n",
    "    yesterday = datetime.today() - timedelta(i)\n",
    "    yesterday = yesterday.strftime('%d/%m/%Y')\n",
    "    get_load_data(yesterday)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:25:42.223950Z",
     "start_time": "2018-11-23T13:25:42.177936Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(31, 0, -1):\n",
    "    yesterday = datetime.today() - timedelta(i)\n",
    "    yesterday = yesterday.strftime('%d/%m/%Y')\n",
    "    try:\n",
    "        print(data[yesterday].shape[0])\n",
    "    except:\n",
    "        print(yesterday, 'not found')\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:23:55.648197Z",
     "start_time": "2018-11-23T13:23:55.499732Z"
    }
   },
   "outputs": [],
   "source": [
    "!head monthdata.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:42:03.029781Z",
     "start_time": "2018-11-23T17:42:03.023893Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T08:47:18.309449Z",
     "start_time": "2018-11-24T08:47:18.205666Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('monthdata.csv', header=None, names=['datetime', 'load'], index_col=[0], parse_dates=[0], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:24:18.222226Z",
     "start_time": "2018-11-23T13:24:18.203222Z"
    }
   },
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:29:40.382773Z",
     "start_time": "2018-11-23T13:29:40.364284Z"
    }
   },
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:29:42.116587Z",
     "start_time": "2018-11-23T13:29:42.107393Z"
    }
   },
   "outputs": [],
   "source": [
    "data.shape[0] / 288 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:29:43.961089Z",
     "start_time": "2018-11-23T13:29:43.938154Z"
    }
   },
   "outputs": [],
   "source": [
    "data['26/10/2018':].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:29:45.962701Z",
     "start_time": "2018-11-23T13:29:45.935782Z"
    }
   },
   "outputs": [],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:31:04.603190Z",
     "start_time": "2018-11-23T13:31:03.628992Z"
    }
   },
   "outputs": [],
   "source": [
    "# data['27/10/2018'].plot()\n",
    "data.plot()\n",
    "plt.rcParams['figure.figsize'] = (20, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:30:39.992777Z",
     "start_time": "2018-11-23T13:30:39.979822Z"
    }
   },
   "outputs": [],
   "source": [
    "data['30/10/2018':'10/11/2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:28:06.555638Z",
     "start_time": "2018-11-23T13:28:06.517372Z"
    }
   },
   "outputs": [],
   "source": [
    "data['11/11/2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:27:05.844988Z",
     "start_time": "2018-11-23T13:27:05.830860Z"
    }
   },
   "outputs": [],
   "source": [
    "set(data.index.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:13:18.200229Z",
     "start_time": "2018-11-23T13:13:17.797801Z"
    }
   },
   "outputs": [],
   "source": [
    "data[:'24/10/2018'].plot()\n",
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-28T07:59:00.899113Z",
     "start_time": "2018-10-28T07:59:00.868404Z"
    }
   },
   "outputs": [],
   "source": [
    "# data = data.asfreq(freq='H', method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:11:18.354280Z",
     "start_time": "2018-11-23T13:11:18.345366Z"
    }
   },
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:11:29.897473Z",
     "start_time": "2018-11-23T13:11:29.881719Z"
    }
   },
   "outputs": [],
   "source": [
    "# number of unique dates in the data\n",
    "len(set(data.index.date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:11:33.065186Z",
     "start_time": "2018-11-23T13:11:33.047142Z"
    }
   },
   "outputs": [],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T13:32:20.576485Z",
     "start_time": "2018-11-23T13:32:17.928008Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 288 #daily freq\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "# result = seasonal_decompose(data[:'24/10/2018'], freq=decompfreq, model='aditive')\n",
    "result = seasonal_decompose(data, freq=decompfreq, model='aditive')\n",
    "\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Making the data stationary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:18:31.569374Z",
     "start_time": "2018-11-23T20:18:31.564175Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detrending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:02:51.549337Z",
     "start_time": "2018-11-23T17:02:49.020376Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 288 #daily freq\n",
    "result = seasonal_decompose(data, freq=decompfreq, model='aditive')\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:18:33.988048Z",
     "start_time": "2018-11-23T20:18:33.979791Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_data = data.diff(1).dropna()  # detrended data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:03:06.535432Z",
     "start_time": "2018-11-23T17:03:03.510740Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 288 #daily freq\n",
    "result = seasonal_decompose(dt_data, freq=decompfreq, model='aditive')\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:03:21.872749Z",
     "start_time": "2018-11-23T17:03:20.857258Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T16:52:57.108317Z",
     "start_time": "2018-11-23T16:52:57.017125Z"
    }
   },
   "outputs": [],
   "source": [
    "# lag_pacf = pacf(dt_data['load'].values, nlags = 2000)\n",
    "lag_acf = acf(dt_data['load'].values, nlags = 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T16:58:10.968047Z",
     "start_time": "2018-11-23T16:58:10.956643Z"
    }
   },
   "outputs": [],
   "source": [
    "np.argsort(lag_pacf)  #returns the indices that would sort the array in ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T16:58:19.377539Z",
     "start_time": "2018-11-23T16:58:19.367265Z"
    }
   },
   "outputs": [],
   "source": [
    "np.argsort(lag_acf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T16:58:24.778884Z",
     "start_time": "2018-11-23T16:58:24.427554Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.subplot(121) \n",
    "plt.plot(lag_pacf)\n",
    "plt.axhline(y=0,linestyle='--')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(dt_data['load'])), linestyle='--')\n",
    "plt.axhline(y=1.96/np.sqrt(len(dt_data['load'])), linestyle='--')\n",
    "\n",
    "# critical value determination: https://stats.stackexchange.com/a/185553/181916"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T16:58:26.711219Z",
     "start_time": "2018-11-23T16:58:26.370923Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.subplot(121) \n",
    "plt.plot(lag_acf)\n",
    "plt.axhline(y=0,linestyle='--')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(dt_data['load'])), linestyle='--')\n",
    "plt.axhline(y=1.96/np.sqrt(len(dt_data['load'])), linestyle='--')\n",
    "\n",
    "# critical value determination: https://stats.stackexchange.com/a/185553/181916"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:06:03.208248Z",
     "start_time": "2018-11-23T21:06:03.199741Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_dt_data = dt_data.diff(288).dropna()  # deseasonalized + detrended data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:03:44.082515Z",
     "start_time": "2018-11-23T17:03:40.379700Z"
    }
   },
   "outputs": [],
   "source": [
    "decompfreq = 288 #daily freq\n",
    "result = seasonal_decompose(ds_dt_data, freq=decompfreq, model='aditive')\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:00:26.278500Z",
     "start_time": "2018-11-23T17:00:25.204831Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_dt_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rescale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:12:25.875421Z",
     "start_time": "2018-11-23T20:12:25.869548Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_dt_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:18:42.731110Z",
     "start_time": "2018-11-23T20:18:42.563234Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:06:05.041709Z",
     "start_time": "2018-11-23T21:06:05.036017Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "scaler = scaler.fit(ds_dt_data['load'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:06:10.134307Z",
     "start_time": "2018-11-23T21:06:09.833655Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_dt_data['load1'] = ds_dt_data.shift(-1)['load']\n",
    "ds_dt_data['load2'] = ds_dt_data.shift(-2)['load']\n",
    "ds_dt_data['load3'] = ds_dt_data.shift(-3)['load']\n",
    "ds_dt_data = ds_dt_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:06:16.122157Z",
     "start_time": "2018-11-23T21:06:16.116033Z"
    }
   },
   "outputs": [],
   "source": [
    "ds_dt_data = scaler.transform(ds_dt_data)  # don't use .values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:06:33.585695Z",
     "start_time": "2018-11-23T21:06:33.581049Z"
    }
   },
   "outputs": [],
   "source": [
    "split_idx = int(0.9 * len(ds_dt_data))\n",
    "train, val = ds_dt_data[:split_idx], ds_dt_data[split_idx:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:06:36.991736Z",
     "start_time": "2018-11-23T21:06:36.987068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7513, 4), (835, 4))"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:02.378529Z",
     "start_time": "2018-11-23T21:26:02.374033Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = train[:, 0], train[:, 1:]\n",
    "X_val, y_val = val[:, 0], val[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:24:29.563159Z",
     "start_time": "2018-11-23T21:24:29.556338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00770444, -0.01085788,  0.06482477],\n",
       "       [-0.01085788,  0.06482477, -0.06976994],\n",
       "       [ 0.06482477, -0.06976994,  0.0990468 ],\n",
       "       ...,\n",
       "       [ 0.03350534, -0.09532   ,  0.15046943],\n",
       "       [-0.09532   ,  0.15046943, -0.10513868],\n",
       "       [ 0.15046943, -0.10513868, -0.10094603]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:18:46.897901Z",
     "start_time": "2018-11-23T20:18:46.160696Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(X)), X)\n",
    "plt.plot(range(len(X), len(X_val) + len(X)), X_val);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:37:29.904237Z",
     "start_time": "2018-11-23T17:37:29.896798Z"
    }
   },
   "outputs": [],
   "source": [
    "# to get the original series back\n",
    "inverted_X = scaler.inverse_transform(scaled_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM model development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T17:22:38.122094Z",
     "start_time": "2018-11-23T17:22:38.115827Z"
    }
   },
   "source": [
    "The LSTM layer expects input to be in a matrix with the dimensions: [samples, time steps, features]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:18:51.539835Z",
     "start_time": "2018-11-23T20:18:51.534813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7513,), (7513, 3))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:18:51.817889Z",
     "start_time": "2018-11-23T20:18:51.814082Z"
    }
   },
   "outputs": [],
   "source": [
    "X = X.reshape(X.shape[0], 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:20:38.510503Z",
     "start_time": "2018-11-23T20:20:38.198384Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(1, batch_input_shape=(1, X.shape[1], X.shape[2]), stateful=True))\n",
    "model.add(Dense(y.shape[1]))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T20:24:25.569447Z",
     "start_time": "2018-11-23T20:20:40.533547Z"
    }
   },
   "outputs": [],
   "source": [
    "# fit network\n",
    "for i in range(10):\n",
    "    model.fit(X, y, epochs=1, batch_size=1, verbose=0, shuffle=False)\n",
    "    model.reset_states()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make one forecast with an LSTM,\n",
    "def forecast_lstm(model, X, n_batch):\n",
    "    # reshape input pattern to [samples, timesteps, features]\n",
    "    X = X.reshape(1, 1, len(X))\n",
    "    # make forecast\n",
    "    forecast = model.predict(X, batch_size=n_batch)\n",
    "    # convert to array\n",
    "    return [x for x in forecast[0, :]]\n",
    "\n",
    "def make_forecasts(model, n_batch, train, test, n_lag, n_seq):\n",
    "    forecasts = list()\n",
    "    for i in range(len(test)):\n",
    "        X, y = test[i, 0:n_lag], test[i, n_lag:]\n",
    "        # make forecast\n",
    "        forecast = forecast_lstm(model, X, n_batch)\n",
    "        # store the forecast\n",
    "        forecasts.append(forecast)\n",
    "    return forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:25:18.651159Z",
     "start_time": "2018-11-23T21:25:17.782135Z"
    }
   },
   "outputs": [],
   "source": [
    "forecasts = make_forecasts(model, 1, train, val, 1, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:46.985068Z",
     "start_time": "2018-11-23T21:26:46.957652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.014558656,\n",
       " 0.003019549,\n",
       " 0.005153097,\n",
       " 0.004762993,\n",
       " 0.0048343698,\n",
       " 0.0048213126,\n",
       " 0.0048237005,\n",
       " 0.004823263,\n",
       " 0.0048233448,\n",
       " 0.004823331,\n",
       " 0.004823331,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317,\n",
       " 0.0048233317]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:23:39.124783Z",
     "start_time": "2018-11-23T21:23:39.119678Z"
    }
   },
   "outputs": [],
   "source": [
    "# y_val_hat = [x[0] for x in forecasts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:08.556001Z",
     "start_time": "2018-11-23T21:26:08.551959Z"
    }
   },
   "outputs": [],
   "source": [
    "y_val_hat = forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:09.640050Z",
     "start_time": "2018-11-23T21:26:09.633412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(835, (835, 3))"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val_hat.__len__(), y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:12.604151Z",
     "start_time": "2018-11-23T21:26:11.879262Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe4b6d6e2b0>]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_val)\n",
    "plt.plot(y_val_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:23.505225Z",
     "start_time": "2018-11-23T21:26:23.499089Z"
    }
   },
   "outputs": [],
   "source": [
    "y_val_hat = scaler.inverse_transform(np.asarray(y_val_hat).reshape(-1, 1))\n",
    "y_val = scaler.inverse_transform(np.asarray(y_val[:, 0]).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:24.947932Z",
     "start_time": "2018-11-23T21:26:24.342974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe4b6d31400>]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_val)\n",
    "plt.plot(y_val_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:26:29.214646Z",
     "start_time": "2018-11-23T21:26:28.430539Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.0527387] [0.14] 1599.1100000000001\n",
      "[-1.1673646] [-5.04] 1629.6299999999999\n",
      "[-0.5719767] [16.08] 1582.9700000000003\n",
      "[-0.6808391] [-21.48] 1605.52\n",
      "[-0.6609208] [25.63] 1604.2499999999998\n",
      "[-0.66456455] [-6.07] 1586.3700000000001\n",
      "[-0.66389817] [3.33] 1610.8300000000002\n",
      "[-0.6640203] [2.] 1579.6100000000001\n",
      "[-0.6639974] [-5.06] 1595.33\n",
      "[-0.66400135] [5.41] 1582.09\n",
      "[-0.66400135] [-2.07] 1587.16\n",
      "[-0.66400105] [1.76] 1579.8100000000002\n",
      "[-0.66400105] [-6.52] 1579.68\n",
      "[-0.66400105] [5.57] 1575.14\n",
      "[-0.66400105] [4.18] 1595.2399999999998\n",
      "[-0.66400105] [5.69] 1587.5700000000002\n",
      "[-0.66400105] [-3.28] 1600.66\n",
      "[-0.66400105] [8.72] 1603.43\n",
      "[-0.66400105] [-23.28] 1579.9699999999998\n",
      "[-0.66400105] [6.2] 1599.02\n",
      "[-0.66400105] [7.27] 1600.81\n",
      "[-0.66400105] [12.97] 1629.12\n",
      "[-0.66400105] [12.54] 1651.64\n",
      "[-0.66400105] [-19.05] 1646.08\n",
      "[-0.66400105] [-6.85] 1630.65\n",
      "[-0.66400105] [-6.26] 1664.2799999999997\n",
      "[-0.66400105] [12.8] 1670.17\n",
      "[-0.66400105] [0.83] 1679.1899999999998\n",
      "[-0.66400105] [14.98] 1728.2300000000002\n",
      "[-0.66400105] [-0.39] 1731.05\n",
      "[-0.66400105] [-42.33] 1711.8200000000002\n",
      "[-0.66400105] [0.98] 1744.3\n",
      "[-0.66400105] [27.25] 1811.24\n",
      "[-0.66400105] [-6.95] 1826.8500000000001\n",
      "[-0.66400105] [-10.09] 1856.6899999999998\n",
      "[-0.66400105] [-8.96] 1907.81\n",
      "[-0.66400105] [-6.94] 1927.62\n",
      "[-0.66400105] [-9.77] 1974.27\n",
      "[-0.66400105] [-9.95] 2033.19\n",
      "[-0.66400105] [11.49] 2069.77\n",
      "[-0.66400105] [-37.11] 2113.67\n",
      "[-0.66400105] [15.15] 2190.9500000000003\n",
      "[-0.66400105] [3.87] 2232.27\n",
      "[-0.66400105] [-29.35] 2261.33\n",
      "[-0.66400105] [-11.2] 2379.04\n",
      "[-0.66400105] [49.32] 2361.2\n",
      "[-0.66400105] [-41.63] 2530.65\n",
      "[-0.66400105] [4.11] 2452.3799999999997\n",
      "[-0.66400105] [3.1] 2713.0800000000004\n",
      "[-0.66400105] [38.68] 2640.56\n",
      "[-0.66400105] [-108.09] 2748.0699999999997\n",
      "[-0.66400105] [31.42] 2712.75\n",
      "[-0.66400105] [-37.55] 2832.35\n",
      "[-0.66400105] [52.52] 2858.32\n",
      "[-0.66400105] [-8.62] 2907.55\n",
      "[-0.66400105] [18.26] 2926.13\n",
      "[-0.66400105] [-34.41] 2921.7\n",
      "[-0.66400105] [-8.54] 3001.85\n",
      "[-0.66400105] [63.13] 2957.42\n",
      "[-0.66400105] [-83.35] 2969.7400000000002\n",
      "[-0.66400105] [43.18] 3008.51\n",
      "[-0.66400105] [22.52] 2996.77\n",
      "[-0.66400105] [-69.55] 2956.77\n",
      "[-0.66400105] [50.21] 2980.9\n",
      "[-0.66400105] [-25.91] 2997.24\n",
      "[-0.66400105] [18.17] 3044.61\n",
      "[-0.66400105] [-68.68] 2856.57\n",
      "[-0.66400105] [-42.99] 2970.45\n",
      "[-0.66400105] [90.72] 2969.52\n",
      "[-0.66400105] [21.74] 3027.9799999999996\n",
      "[-0.66400105] [39.44] 3052.25\n",
      "[-0.66400105] [-4.03] 3099.8799999999997\n",
      "[-0.66400105] [-62.26] 3009.6800000000003\n",
      "[-0.66400105] [-47.65] 2963.34\n",
      "[-0.66400105] [22.86] 3059.18\n",
      "[-0.66400105] [7.45] 2975.8800000000006\n",
      "[-0.66400105] [1.4] 2999.2\n",
      "[-0.66400105] [0.63] 3000.97\n",
      "[-0.66400105] [37.04] 3008.89\n",
      "[-0.66400105] [-7.07] 3006.31\n",
      "[-0.66400105] [18.82] 2989.5400000000004\n",
      "[-0.66400105] [11.34] 3052.24\n",
      "[-0.66400105] [-19.08] 3005.5\n",
      "[-0.66400105] [7.52] 3107.58\n",
      "[-0.66400105] [9.45] 3016.3900000000003\n",
      "[-0.66400105] [-56.53] 3088.7599999999998\n",
      "[-0.66400105] [89.62] 3142.77\n",
      "[-0.66400105] [-49.07] 3149.02\n",
      "[-0.66400105] [24.1] 3177.1800000000003\n",
      "[-0.66400105] [-2.45] 3174.9700000000003\n",
      "[-0.66400105] [-63.55] 3135.66\n",
      "[-0.66400105] [17.03] 3222.3\n",
      "[-0.66400105] [26.14] 3195.06\n",
      "[-0.66400105] [-12.78] 3330.91\n",
      "[-0.66400105] [-16.55] 3035.78\n",
      "[-0.66400105] [-142.83] 3194.1600000000003\n",
      "[-0.66400105] [174.59] 3203.6\n",
      "[-0.66400105] [-67.68] 3256.7000000000003\n",
      "[-0.66400105] [28.18] 3121.88\n",
      "[-0.66400105] [-102.51] 3206.5699999999997\n",
      "[-0.66400105] [136.] 3265.26\n",
      "[-0.66400105] [-72.8] 3227.89\n",
      "[-0.66400105] [5.23] 3193.85\n",
      "[-0.66400105] [-22.42] 3178.45\n",
      "[-0.66400105] [1.3] 3191.11\n",
      "[-0.66400105] [27.04] 3136.24\n",
      "[-0.66400105] [-7.18] 3233.6800000000003\n",
      "[-0.66400105] [67.96] 3171.9799999999996\n",
      "[-0.66400105] [-44.61] 3205.76\n",
      "[-0.66400105] [33.93] 3147.1299999999997\n",
      "[-0.66400105] [-45.07] 3141.8\n",
      "[-0.66400105] [33.67] 3190.99\n",
      "[-0.66400105] [32.62] 3127.59\n",
      "[-0.66400105] [-92.74] 3065.48\n",
      "[-0.66400105] [34.74] 3136.8199999999997\n",
      "[-0.66400105] [78.59] 3168.02\n",
      "[-0.66400105] [-94.13] 3061.66\n",
      "[-0.66400105] [20.83] 3085.73\n",
      "[-0.66400105] [23.61] 3096.25\n",
      "[-0.66400105] [-39.12] 3057.89\n",
      "[-0.66400105] [52.24] 3050.27\n",
      "[-0.66400105] [19.97] 3125.5099999999998\n",
      "[-0.66400105] [-5.22] 3032.0499999999997\n",
      "[-0.66400105] [-40.16] 3102.7200000000003\n",
      "[-0.66400105] [-11.87] 2935.4\n",
      "[-0.66400105] [-21.44] 3011.47\n",
      "[-0.66400105] [5.04] 2983.3900000000003\n",
      "[-0.66400105] [35.06] 2908.31\n",
      "[-0.66400105] [-22.46] 2887.25\n",
      "[-0.66400105] [-0.38] 2876.5099999999998\n",
      "[-0.66400105] [-23.98] 2759.9\n",
      "[-0.66400105] [-3.88] 2751.45\n",
      "[-0.66400105] [-23.11] 2658.0000000000005\n",
      "[-0.66400105] [7.29] 2713.8999999999996\n",
      "[-0.66400105] [77.88] 2754.7799999999997\n",
      "[-0.66400105] [-61.84] 2669.91\n",
      "[-0.66400105] [27.28] 2765.96\n",
      "[-0.66400105] [45.85] 2766.3700000000003\n",
      "[-0.66400105] [-16.79] 2805.0199999999995\n",
      "[-0.66400105] [13.94] 2777.67\n",
      "[-0.66400105] [-43.13] 2783.86\n",
      "[-0.66400105] [39.99] 2803.8599999999997\n",
      "[-0.66400105] [-8.15] 2834.12\n",
      "[-0.66400105] [13.47] 2815.05\n",
      "[-0.66400105] [-112.85] 2706.96\n",
      "[-0.66400105] [67.75] 2812.71\n",
      "[-0.66400105] [0.02] 2764.7000000000003\n",
      "[-0.66400105] [48.84] 2830.42\n",
      "[-0.66400105] [-32.22] 2780.7400000000002\n",
      "[-0.66400105] [3.71] 2842.43\n",
      "[-0.66400105] [-3.16] 2759.1\n",
      "[-0.66400105] [-40.66] 2806.6\n",
      "[-0.66400105] [37.42] 2820.44\n",
      "[-0.66400105] [-38.2] 2729.05\n",
      "[-0.66400105] [5.73] 2793.41\n",
      "[-0.66400105] [14.82] 2755.27\n",
      "[-0.66400105] [5.94] 2781.71\n",
      "[-0.66400105] [-50.17] 2712.49\n",
      "[-0.66400105] [36.62] 2771.0499999999997\n",
      "[-0.66400105] [-10.33] 2695.5400000000004\n",
      "[-0.66400105] [-45.97] 2743.1200000000003\n",
      "[-0.66400105] [76.67] 2744.27\n",
      "[-0.66400105] [-39.26] 2743.27\n",
      "[-0.66400105] [-2.97] 2727.06\n",
      "[-0.66400105] [-45.36] 2645.34\n",
      "[-0.66400105] [7.85] 2716.6\n",
      "[-0.66400105] [65.98] 2742.28\n",
      "[-0.66400105] [0.59] 2637.47\n",
      "[-0.66400105] [-7.32] 2841.02\n",
      "[-0.66400105] [122.44] 2798.2999999999997\n",
      "[-0.66400105] [-98.04] 2810.35\n",
      "[-0.66400105] [26.04] 2729.5300000000007\n",
      "[-0.66400105] [-12.75] 2906.46\n",
      "[-0.66400105] [65.36] 2722.71\n",
      "[-0.66400105] [-76.67] 2901.77\n",
      "[-0.66400105] [130.28] 2818.94\n",
      "[-0.66400105] [-74.2] 2914.9500000000003\n",
      "[-0.66400105] [38.23] 2862.8\n",
      "[-0.66400105] [-43.46] 2914.28\n",
      "[-0.66400105] [29.7] 2973.57\n",
      "[-0.66400105] [-1.07] 2958.74\n",
      "[-0.66400105] [-22.33] 3007.81\n",
      "[-0.66400105] [-15.55] 2992.63\n",
      "[-0.66400105] [13.83] 3083.5699999999997\n",
      "[-0.66400105] [2.22] 3073.2900000000004\n",
      "[-0.66400105] [-44.88] 3041.86\n",
      "[-0.66400105] [62.11] 3187.89\n",
      "[-0.66400105] [8.36] 3123.3600000000006\n",
      "[-0.66400105] [-75.52] 3154.2\n",
      "[-0.66400105] [-5.48] 3166.81\n",
      "[-0.66400105] [-89.02] 3084.46\n",
      "[-0.66400105] [-29.31] 3062.25\n",
      "[-0.66400105] [-66.08] 3026.4700000000003\n",
      "[-0.66400105] [27.74] 2989.52\n",
      "[-0.66400105] [36.93] 3113.48\n",
      "[-0.66400105] [158.06] 3039.1\n",
      "[-0.66400105] [-82.69] 3157.04\n",
      "[-0.66400105] [122.4] 3061.57\n",
      "[-0.66400105] [-52.] 3177.73\n",
      "[-0.66400105] [69.2] 3081.34\n",
      "[-0.66400105] [-52.58] 3106.17\n",
      "[-0.66400105] [-11.88] 3078.28\n",
      "[-0.66400105] [50.13] 3102.59\n",
      "[-0.66400105] [-7.66] 3042.45\n",
      "[-0.66400105] [34.15] 3134.88\n",
      "[-0.66400105] [15.72] 3106.46\n",
      "[-0.66400105] [-12.68] 3014.1299999999997\n",
      "[-0.66400105] [-51.61] 3047.2200000000003\n",
      "[-0.66400105] [85.42] 3007.47\n",
      "[-0.66400105] [-48.06] 3010.4\n",
      "[-0.66400105] [39.54] 2991.71\n",
      "[-0.66400105] [13.14] 2956.4399999999996\n",
      "[-0.66400105] [-11.64] 3011.8\n",
      "[-0.66400105] [-1.92] 2897.3700000000003\n",
      "[-0.66400105] [-65.72] 2860.4199999999996\n",
      "[-0.66400105] [60.58] 2906.82\n",
      "[-0.66400105] [-2.91] 2865.3\n",
      "[-0.66400105] [-23.52] 2794.96\n",
      "[-0.66400105] [-33.62] 2799.5199999999995\n",
      "[-0.66400105] [28.76] 2743.1500000000005\n",
      "[-0.66400105] [-39.73] 2715.93\n",
      "[-0.66400105] [43.45] 2709.43\n",
      "[-0.66400105] [-25.84] 2654.6299999999997\n",
      "[-0.66400105] [8.63] 2669.41\n",
      "[-0.66400105] [40.65] 2580.1700000000005\n",
      "[-0.66400105] [-103.67] 2550.81\n",
      "[-0.66400105] [87.53] 2551.33\n",
      "[-0.66400105] [18.46] 2559.85\n",
      "[-0.66400105] [-7.2] 2584.61\n",
      "[-0.66400105] [-15.69] 2511.62\n",
      "[-0.66400105] [-78.56] 2432.73\n",
      "[-0.66400105] [36.65] 2504.7900000000004\n",
      "[-0.66400105] [5.51] 2400.8599999999997\n",
      "[-0.66400105] [-29.99] 2474.61\n",
      "[-0.66400105] [67.19] 2366.8300000000004\n",
      "[-0.66400105] [-66.87] 2432.6099999999997\n",
      "[-0.66400105] [12.49] 2331.0\n",
      "[-0.66400105] [22.29] 2351.78\n",
      "[-0.66400105] [-4.9] 2353.29\n",
      "[-0.66400105] [7.19] 2323.29\n",
      "[-0.66400105] [-3.4] 2301.44\n",
      "[-0.66400105] [-24.2] 2253.56\n",
      "[-0.66400105] [27.23] 2286.79\n",
      "[-0.66400105] [-10.23] 2236.0499999999997\n",
      "[-0.66400105] [-28.86] 2189.5099999999998\n",
      "[-0.66400105] [9.93] 2186.91\n",
      "[-0.66400105] [10.66] 2154.1000000000004\n",
      "[-0.66400105] [2.14] 2162.2799999999997\n",
      "[-0.66400105] [1.35] 2111.05\n",
      "[-0.66400105] [-8.31] 2120.57\n",
      "[-0.66400105] [19.14] 2064.95\n",
      "[-0.66400105] [-28.84] 2037.28\n",
      "[-0.66400105] [34.89] 2102.91\n",
      "[-0.66400105] [27.84] 2020.5500000000002\n",
      "[-0.66400105] [-71.44] 2008.18\n",
      "[-0.66400105] [14.47] 1963.21\n",
      "[-0.66400105] [3.89] 1995.3\n",
      "[-0.66400105] [6.69] 1918.41\n",
      "[-0.66400105] [-44.87] 1918.04\n",
      "[-0.66400105] [41.18] 1897.1499999999999\n",
      "[-0.66400105] [-1.12] 1901.3\n",
      "[-0.66400105] [-17.34] 1843.77\n",
      "[-0.66400105] [2.92] 1853.78\n",
      "[-0.66400105] [-9.47] 1827.9700000000003\n",
      "[-0.66400105] [22.84] 1823.1599999999999\n",
      "[-0.66400105] [-22.87] 1809.63\n",
      "[-0.66400105] [3.] 1772.75\n",
      "[-0.66400105] [6.83] 1794.36\n",
      "[-0.66400105] [1.26] 1757.61\n",
      "[-0.66400105] [3.71] 1763.5\n",
      "[-0.66400105] [8.69] 1759.44\n",
      "[-0.66400105] [-18.06] 1729.79\n",
      "[-0.66400105] [-8.89] 1709.51\n",
      "[-0.66400105] [23.83] 1733.23\n",
      "[-0.66400105] [-15.14] 1705.97\n",
      "[-0.66400105] [-5.1] 1680.8400000000001\n",
      "[-0.66400105] [-2.99] 1680.28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.66400105] [1.6] 1670.3500000000001\n",
      "[-0.66400105] [8.56] 1661.78\n",
      "[-0.66400105] [-5.31] 1676.02\n",
      "[-0.66400105] [18.88] 1653.12\n",
      "[-0.66400105] [-14.48] 1672.13\n",
      "[-0.66400105] [-4.61] 1633.4499999999998\n",
      "[-0.66400105] [-8.32] 1651.45\n",
      "[-0.66400105] [9.29] 1623.7500000000002\n",
      "[-0.66400105] [-8.46] 1634.61\n",
      "[-0.66400105] [0.66] 1616.23\n",
      "[-0.66400105] [-2.83] 1611.99\n",
      "[-0.66400105] [4.16] 1609.74\n",
      "[-0.66400105] [0.76] 1603.4599999999998\n",
      "[-0.66400105] [-1.96] 1601.95\n",
      "[-0.66400105] [14.14] 1606.94\n",
      "[-0.66400105] [-14.38] 1600.69\n",
      "[-0.66400105] [7.55] 1589.03\n",
      "[-0.66400105] [-2.64] 1603.79\n",
      "[-0.66400105] [-1.12] 1581.9199999999998\n",
      "[-0.66400105] [-4.15] 1586.0100000000002\n",
      "[-0.66400105] [5.62] 1582.22\n",
      "[-0.66400105] [10.72] 1589.73\n",
      "[-0.66400105] [5.28] 1599.75\n",
      "[-0.66400105] [-5.17] 1593.0599999999997\n",
      "[-0.66400105] [-14.67] 1575.3600000000001\n",
      "[-0.66400105] [1.94] 1588.43\n",
      "[-0.66400105] [-4.44] 1588.32\n",
      "[-0.66400105] [5.08] 1574.4899999999998\n",
      "[-0.66400105] [-13.07] 1593.67\n",
      "[-0.66400105] [31.27] 1602.5500000000002\n",
      "[-0.66400105] [-22.73] 1604.57\n",
      "[-0.66400105] [11.03] 1616.96\n",
      "[-0.66400105] [-16.69] 1596.99\n",
      "[-0.66400105] [-16.09] 1615.62\n",
      "[-0.66400105] [19.75] 1630.3999999999999\n",
      "[-0.66400105] [24.76] 1658.79\n",
      "[-0.66400105] [-15.52] 1664.93\n",
      "[-0.66400105] [2.01] 1686.9800000000002\n",
      "[-0.66400105] [3.52] 1684.15\n",
      "[-0.66400105] [-18.99] 1672.26\n",
      "[-0.66400105] [17.95] 1736.34\n",
      "[-0.66400105] [12.66] 1756.04\n",
      "[-0.66400105] [-8.19] 1746.1299999999999\n",
      "[-0.66400105] [-10.57] 1799.3600000000001\n",
      "[-0.66400105] [-26.58] 1789.52\n",
      "[-0.66400105] [-3.47] 1817.9099999999999\n",
      "[-0.66400105] [-10.] 1856.2\n",
      "[-0.66400105] [-5.45] 1870.38\n",
      "[-0.66400105] [-18.15] 1920.32\n",
      "[-0.66400105] [6.65] 1937.2900000000002\n",
      "[-0.66400105] [-30.22] 1995.9099999999999\n",
      "[-0.66400105] [-5.06] 2023.4699999999998\n",
      "[-0.66400105] [-31.65] 2035.8800000000003\n",
      "[-0.66400105] [-34.12] 2061.2299999999996\n",
      "[-0.66400105] [-30.22] 2120.59\n",
      "[-0.66400105] [8.32] 2155.67\n",
      "[-0.66400105] [-64.82] 2118.75\n",
      "[-0.66400105] [-17.37] 2269.82\n",
      "[-0.66400105] [26.41] 2253.54\n",
      "[-0.66400105] [-74.55] 2292.9199999999996\n",
      "[-0.66400105] [-38.05] 2321.86\n",
      "[-0.66400105] [9.68] 2370.08\n",
      "[-0.66400105] [-130.99] 2293.84\n",
      "[-0.66400105] [38.36] 2390.6600000000003\n",
      "[-0.66400105] [-0.54] 2442.97\n",
      "[-0.66400105] [-3.37] 2436.1600000000003\n",
      "[-0.66400105] [-15.65] 2464.9599999999996\n",
      "[-0.66400105] [24.39] 2556.59\n",
      "[-0.66400105] [74.37] 2564.63\n",
      "[-0.66400105] [-2.09] 2644.64\n",
      "[-0.66400105] [46.32] 2682.62\n",
      "[-0.66400105] [31.] 2660.3199999999997\n",
      "[-0.66400105] [32.92] 2801.2599999999998\n",
      "[-0.66400105] [69.29] 2754.4300000000003\n",
      "[-0.66400105] [5.35] 2827.99\n",
      "[-0.66400105] [103.85] 2861.3300000000004\n",
      "[-0.66400105] [-15.43] 2918.9599999999996\n",
      "[-0.66400105] [79.41] 2944.0400000000004\n",
      "[-0.66400105] [43.99] 3032.93\n",
      "[-0.66400105] [-109.48] 2940.22\n",
      "[-0.66400105] [67.87] 3023.08\n",
      "[-0.66400105] [-55.87] 2933.2500000000005\n",
      "[-0.66400105] [46.41] 3041.8999999999996\n",
      "[-0.66400105] [34.17] 3056.38\n",
      "[-0.66400105] [35.54] 3030.17\n",
      "[-0.66400105] [1.33] 3121.29\n",
      "[-0.66400105] [60.21] 3097.43\n",
      "[-0.66400105] [-31.15] 3126.0099999999998\n",
      "[-0.66400105] [-2.69] 3080.98\n",
      "[-0.66400105] [43.15] 3157.94\n",
      "[-0.66400105] [15.51] 3163.39\n",
      "[-0.66400105] [-6.16] 3110.0400000000004\n",
      "[-0.66400105] [4.49] 3203.83\n",
      "[-0.66400105] [19.01] 3178.03\n",
      "[-0.66400105] [-31.5] 3182.6299999999997\n",
      "[-0.66400105] [41.64] 3279.23\n",
      "[-0.66400105] [-32.84] 3180.07\n",
      "[-0.66400105] [-14.82] 3292.43\n",
      "[-0.66400105] [45.74] 3317.87\n",
      "[-0.66400105] [57.62] 3342.55\n",
      "[-0.66400105] [-67.8] 3353.12\n",
      "[-0.66400105] [67.84] 3390.76\n",
      "[-0.66400105] [-7.96] 3430.52\n",
      "[-0.66400105] [0.14] 3399.65\n",
      "[-0.66400105] [15.76] 3424.98\n",
      "[-0.66400105] [-17.65] 3429.62\n",
      "[-0.66400105] [37.75] 3383.4800000000005\n",
      "[-0.66400105] [35.7] 3524.48\n",
      "[-0.66400105] [22.61] 3469.5\n",
      "[-0.66400105] [-42.71] 3530.48\n",
      "[-0.66400105] [10.45] 3416.82\n",
      "[-0.66400105] [-88.27] 3465.27\n",
      "[-0.66400105] [53.65] 3453.9\n",
      "[-0.66400105] [38.95] 3482.5299999999997\n",
      "[-0.66400105] [7.69] 3500.5600000000004\n",
      "[-0.66400105] [-24.87] 3454.13\n",
      "[-0.66400105] [-1.84] 3472.56\n",
      "[-0.66400105] [-0.26] 3457.17\n",
      "[-0.66400105] [-19.75] 3454.26\n",
      "[-0.66400105] [5.78] 3414.82\n",
      "[-0.66400105] [-2.53] 3432.3999999999996\n",
      "[-0.66400105] [-4.36] 3421.66\n",
      "[-0.66400105] [52.61] 3398.1\n",
      "[-0.66400105] [-24.46] 3437.7200000000003\n",
      "[-0.66400105] [-12.09] 3407.37\n",
      "[-0.66400105] [19.79] 3325.78\n",
      "[-0.66400105] [-57.44] 3414.5\n",
      "[-0.66400105] [53.4] 3364.32\n",
      "[-0.66400105] [-56.55] 3269.1099999999997\n",
      "[-0.66400105] [-24.48] 3346.51\n",
      "[-0.66400105] [36.3] 3312.18\n",
      "[-0.66400105] [-41.25] 3238.1200000000003\n",
      "[-0.66400105] [-15.36] 3263.06\n",
      "[-0.66400105] [39.52] 3237.41\n",
      "[-0.66400105] [-5.26] 3293.25\n",
      "[-0.66400105] [46.05] 3198.46\n",
      "[-0.66400105] [-44.82] 3259.7299999999996\n",
      "[-0.66400105] [25.49] 3199.6200000000003\n",
      "[-0.66400105] [-77.8] 3068.82\n",
      "[-0.66400105] [18.2] 3042.36\n",
      "[-0.66400105] [2.54] 3054.26\n",
      "[-0.66400105] [41.22] 2959.64\n",
      "[-0.66400105] [-38.65] 2942.9399999999996\n",
      "[-0.66400105] [-12.5] 2907.58\n",
      "[-0.66400105] [-8.18] 2815.58\n",
      "[-0.66400105] [-19.27] 2926.31\n",
      "[-0.66400105] [-0.41] 2859.6\n",
      "[-0.66400105] [-5.15] 2887.86\n",
      "[-0.66400105] [-70.03] 2848.9700000000003\n",
      "[-0.66400105] [22.97] 2853.52\n",
      "[-0.66400105] [-39.07] 2870.22\n",
      "[-0.66400105] [37.08] 2865.35\n",
      "[-0.66400105] [-19.96] 2887.01\n",
      "[-0.66400105] [-11.59] 2779.36\n",
      "[-0.66400105] [-60.96] 2879.9300000000003\n",
      "[-0.66400105] [138.31] 2842.42\n",
      "[-0.66400105] [-45.18] 2951.81\n",
      "[-0.66400105] [11.57] 2834.31\n",
      "[-0.66400105] [-54.12] 2881.84\n",
      "[-0.66400105] [46.8] 2872.09\n",
      "[-0.66400105] [-25.48] 2870.8\n",
      "[-0.66400105] [-11.39] 2854.16\n",
      "[-0.66400105] [-27.57] 2798.4700000000003\n",
      "[-0.66400105] [26.81] 2883.0499999999997\n",
      "[-0.66400105] [1.92] 2802.67\n",
      "[-0.66400105] [-32.24] 2825.9100000000003\n",
      "[-0.66400105] [32.49] 2764.15\n",
      "[-0.66400105] [-42.88] 2834.5\n",
      "[-0.66400105] [80.96] 2799.2900000000004\n",
      "[-0.66400105] [-69.66] 2752.55\n",
      "[-0.66400105] [5.16] 2804.8399999999997\n",
      "[-0.66400105] [6.62] 2743.11\n",
      "[-0.66400105] [9.15] 2789.7000000000003\n",
      "[-0.66400105] [-24.95] 2721.3599999999997\n",
      "[-0.66400105] [-11.35] 2747.01\n",
      "[-0.66400105] [72.03] 2795.45\n",
      "[-0.66400105] [-5.96] 2795.36\n",
      "[-0.66400105] [-0.11] 2813.02\n",
      "[-0.66400105] [-18.01] 2819.96\n",
      "[-0.66400105] [-10.44] 2792.59\n",
      "[-0.66400105] [-113.76] 2760.19\n",
      "[-0.66400105] [89.66] 2823.1099999999997\n",
      "[-0.66400105] [-65.22] 2755.1800000000003\n",
      "[-0.66400105] [24.57] 2814.65\n",
      "[-0.66400105] [14.51] 2852.36\n",
      "[-0.66400105] [-5.04] 2821.9\n",
      "[-0.66400105] [-48.58] 2802.92\n",
      "[-0.66400105] [65.51] 2882.75\n",
      "[-0.66400105] [-30.34] 2873.4199999999996\n",
      "[-0.66400105] [-5.04] 2889.0899999999997\n",
      "[-0.66400105] [41.03] 2968.15\n",
      "[-0.66400105] [9.58] 2969.68\n",
      "[-0.66400105] [-46.95] 3001.1800000000003\n",
      "[-0.66400105] [26.23] 3000.62\n",
      "[-0.66400105] [-28.71] 3015.75\n",
      "[-0.66400105] [46.39] 3158.85\n",
      "[-0.66400105] [35.62] 3109.29\n",
      "[-0.66400105] [-81.66] 3089.7799999999997\n",
      "[-0.66400105] [34.53] 3129.8199999999997\n",
      "[-0.66400105] [10.2] 3087.32\n",
      "[-0.66400105] [97.93] 3257.5699999999997\n",
      "[-0.66400105] [53.06] 3191.51\n",
      "[-0.66400105] [-64.53] 3198.59\n",
      "[-0.66400105] [30.83] 3202.83\n",
      "[-0.66400105] [-21.2] 3265.81\n",
      "[-0.66400105] [25.05] 3174.0700000000006\n",
      "[-0.66400105] [-129.04] 3175.74\n",
      "[-0.66400105] [65.5] 3184.78\n",
      "[-0.66400105] [-83.82] 3126.69\n",
      "[-0.66400105] [72.03] 3193.1\n",
      "[-0.66400105] [-77.05] 3060.3599999999997\n",
      "[-0.66400105] [25.92] 3173.12\n",
      "[-0.66400105] [40.41] 3127.85\n",
      "[-0.66400105] [-37.95] 3156.02\n",
      "[-0.66400105] [23.59] 3101.0399999999995\n",
      "[-0.66400105] [-51.5] 3113.57\n",
      "[-0.66400105] [-46.62] 2999.6\n",
      "[-0.66400105] [71.72] 3116.02\n",
      "[-0.66400105] [-17.13] 2998.5000000000005\n",
      "[-0.66400105] [-183.76] 2862.59\n",
      "[-0.66400105] [140.38] 2979.5\n",
      "[-0.66400105] [32.45] 2968.48\n",
      "[-0.66400105] [-53.57] 2889.36\n",
      "[-0.66400105] [5.81] 2886.73\n",
      "[-0.66400105] [23.83] 2922.4300000000003\n",
      "[-0.66400105] [29.65] 2851.64\n",
      "[-0.66400105] [-46.69] 2830.7400000000002\n",
      "[-0.66400105] [18.86] 2797.98\n",
      "[-0.66400105] [14.29] 2804.3100000000004\n",
      "[-0.66400105] [17.41] 2757.7899999999995\n",
      "[-0.66400105] [25.32] 2809.92\n",
      "[-0.66400105] [20.56] 2733.97\n",
      "[-0.66400105] [-88.71] 2673.23\n",
      "[-0.66400105] [52.8] 2703.2500000000005\n",
      "[-0.66400105] [6.06] 2579.77\n",
      "[-0.66400105] [-57.86] 2623.87\n",
      "[-0.66400105] [60.15] 2585.59\n",
      "[-0.66400105] [-26.85] 2533.6\n",
      "[-0.66400105] [-48.2] 2485.4300000000003\n",
      "[-0.66400105] [3.76] 2451.08\n",
      "[-0.66400105] [15.89] 2509.19\n",
      "[-0.66400105] [71.5] 2470.66\n",
      "[-0.66400105] [-55.11] 2452.11\n",
      "[-0.66400105] [13.63] 2458.9900000000002\n",
      "[-0.66400105] [32.06] 2430.74\n",
      "[-0.66400105] [-47.87] 2354.45\n",
      "[-0.66400105] [21.76] 2451.79\n",
      "[-0.66400105] [60.24] 2383.23\n",
      "[-0.66400105] [-44.79] 2371.31\n",
      "[-0.66400105] [-61.37] 2300.86\n",
      "[-0.66400105] [5.05] 2255.1099999999997\n",
      "[-0.66400105] [63.64] 2348.33\n",
      "[-0.66400105] [-30.38] 2232.61\n",
      "[-0.66400105] [-45.44] 2201.77\n",
      "[-0.66400105] [43.44] 2233.26\n",
      "[-0.66400105] [26.44] 2213.8900000000003\n",
      "[-0.66400105] [-52.73] 2150.04\n",
      "[-0.66400105] [21.91] 2138.35\n",
      "[-0.66400105] [-32.3] 2104.5699999999997\n",
      "[-0.66400105] [57.] 2137.79\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.66400105] [-19.2] 2080.13\n",
      "[-0.66400105] [-31.6] 2056.64\n",
      "[-0.66400105] [35.31] 2057.2300000000005\n",
      "[-0.66400105] [-28.77] 1994.8099999999997\n",
      "[-0.66400105] [27.38] 2025.74\n",
      "[-0.66400105] [-9.2] 1993.59\n",
      "[-0.66400105] [-12.92] 1938.5\n",
      "[-0.66400105] [-8.46] 1910.57\n",
      "[-0.66400105] [17.45] 1937.18\n",
      "[-0.66400105] [-5.54] 1888.9699999999998\n",
      "[-0.66400105] [8.56] 1860.42\n",
      "[-0.66400105] [-8.31] 1876.52\n",
      "[-0.66400105] [19.65] 1826.25\n",
      "[-0.66400105] [-42.04] 1826.53\n",
      "[-0.66400105] [36.9] 1791.91\n",
      "[-0.66400105] [-23.74] 1789.2299999999998\n",
      "[-0.66400105] [14.76] 1794.29\n",
      "[-0.66400105] [-4.66] 1747.18\n",
      "[-0.66400105] [-20.1] 1751.14\n",
      "[-0.66400105] [31.01] 1750.3799999999999\n",
      "[-0.66400105] [-6.42] 1723.1000000000001\n",
      "[-0.66400105] [1.22] 1729.43\n",
      "[-0.66400105] [-2.84] 1729.66\n",
      "[-0.66400105] [8.66] 1679.07\n",
      "[-0.66400105] [-15.78] 1697.05\n",
      "[-0.66400105] [14.27] 1686.17\n",
      "[-0.66400105] [-0.1] 1665.53\n",
      "[-0.66400105] [-6.45] 1665.4799999999998\n",
      "[-0.66400105] [14.36] 1656.0400000000002\n",
      "[-0.66400105] [0.64] 1672.3\n",
      "[-0.66400105] [-18.47] 1634.71\n",
      "[-0.66400105] [-8.15] 1613.5299999999997\n",
      "[-0.66400105] [-6.99] 1621.8400000000001\n",
      "[-0.66400105] [-14.19] 1586.5800000000002\n",
      "[-0.66400105] [16.95] 1603.7799999999997\n",
      "[-0.66400105] [22.95] 1608.1500000000003\n",
      "[-0.66400105] [-8.52] 1606.9999999999998\n",
      "[-0.66400105] [9.] 1604.61\n",
      "[-0.66400105] [5.69] 1602.66\n",
      "[-0.66400105] [0.64] 1598.33\n",
      "[-0.66400105] [0.23] 1608.3799999999999\n",
      "[-0.66400105] [3.96] 1582.3100000000002\n",
      "[-0.66400105] [-19.46] 1589.7699999999998\n",
      "[-0.66400105] [15.29] 1588.6200000000001\n",
      "[-0.66400105] [-8.01] 1570.47\n",
      "[-0.66400105] [-6.53] 1575.67\n",
      "[-0.66400105] [6.76] 1570.33\n",
      "[-0.66400105] [2.97] 1582.49\n",
      "[-0.66400105] [4.73] 1577.99\n",
      "[-0.66400105] [-6.9] 1568.1499999999999\n",
      "[-0.66400105] [-10.65] 1558.0200000000002\n",
      "[-0.66400105] [-3.89] 1564.05\n",
      "[-0.66400105] [19.44] 1559.41\n",
      "[-0.66400105] [-9.71] 1572.29\n",
      "[-0.66400105] [-7.81] 1546.22\n",
      "[-0.66400105] [0.88] 1577.61\n",
      "[-0.66400105] [17.27] 1560.06\n",
      "[-0.66400105] [-21.25] 1581.4499999999998\n",
      "[-0.66400105] [18.74] 1574.49\n",
      "[-0.66400105] [-5.05] 1582.43\n",
      "[-0.66400105] [-3.92] 1594.38\n",
      "[-0.66400105] [31.49] 1649.51\n",
      "[-0.66400105] [-25.26] 1598.7500000000002\n",
      "[-0.66400105] [-21.3] 1623.3700000000001\n",
      "[-0.66400105] [8.09] 1639.11\n",
      "[-0.66400105] [4.35] 1643.0\n",
      "[-0.66400105] [4.03] 1681.14\n",
      "[-0.66400105] [14.37] 1678.33\n",
      "[-0.66400105] [-31.15] 1690.41\n",
      "[-0.66400105] [16.77] 1724.5000000000002\n",
      "[-0.66400105] [1.19] 1699.77\n",
      "[-0.66400105] [-3.75] 1772.36\n",
      "[-0.66400105] [37.04] 1793.0300000000002\n",
      "[-0.66400105] [22.88] 1848.85\n",
      "[-0.66400105] [2.03] 1876.4700000000003\n",
      "[-0.66400105] [17.07] 1932.5199999999998\n",
      "[-0.66400105] [14.76] 1960.3500000000001\n",
      "[-0.66400105] [-7.27] 1995.21\n",
      "[-0.66400105] [15.22] 2042.46\n",
      "[-0.66400105] [26.44] 2093.99\n",
      "[-0.66400105] [33.5] 2143.8\n",
      "[-0.66400105] [36.39] 2244.08\n",
      "[-0.66400105] [12.79] 2243.0899999999997\n",
      "[-0.66400105] [11.46] 2337.08\n",
      "[-0.66400105] [42.31] 2386.25\n",
      "[-0.66400105] [28.87] 2465.23\n",
      "[-0.66400105] [15.18] 2500.6300000000006\n",
      "[-0.66400105] [43.45] 2631.1899999999996\n",
      "[-0.66400105] [56.75] 2576.21\n",
      "[-0.66400105] [-23.94] 2769.84\n",
      "[-0.66400105] [142.55] 2797.25\n",
      "[-0.66400105] [-59.32] 2831.92\n",
      "[-0.66400105] [23.14] 2895.0899999999997\n",
      "[-0.66400105] [-8.27] 2920.05\n",
      "[-0.66400105] [-31.85] 2966.98\n",
      "[-0.66400105] [20.38] 3002.53\n",
      "[-0.66400105] [-75.51] 2983.47\n",
      "[-0.66400105] [1.03] 3049.19\n",
      "[-0.66400105] [-19.8] 3069.29\n",
      "[-0.66400105] [-55.01] 3028.3500000000004\n",
      "[-0.66400105] [-48.54] 3056.81\n",
      "[-0.66400105] [-10.52] 3097.96\n",
      "[-0.66400105] [-67.69] 2984.5499999999997\n",
      "[-0.66400105] [-63.44] 3049.2900000000004\n",
      "[-0.66400105] [20.5] 3092.0\n",
      "[-0.66400105] [-61.37] 2902.24\n",
      "[-0.66400105] [-134.28] 3034.2000000000003\n",
      "[-0.66400105] [150.07] 2967.82\n",
      "[-0.66400105] [-93.78] 3059.18\n",
      "[-0.66400105] [37.67] 2994.78\n",
      "[-0.66400105] [-37.61] 3067.19\n",
      "[-0.66400105] [-10.68] 3036.78\n",
      "[-0.66400105] [-93.53] 2975.9199999999996\n",
      "[-0.66400105] [-64.76] 2910.92\n",
      "[-0.66400105] [-67.23] 2848.29\n",
      "[-0.66400105] [-8.38] 2914.33\n",
      "[-0.66400105] [264.3] 3105.9600000000005\n",
      "[-0.66400105] [-12.92] 3148.33\n",
      "[-0.66400105] [-9.08] 3155.35\n",
      "[-0.66400105] [0.91] 3117.4300000000003\n",
      "[-0.66400105] [-71.92] 3088.79\n",
      "[-0.66400105] [39.79] 3175.92\n",
      "[-0.66400105] [49.97] 3156.0099999999998\n",
      "[-0.66400105] [-54.85] 3215.7799999999997\n",
      "[-0.66400105] [-23.32] 3153.86\n",
      "[-0.66400105] [-37.88] 3240.2200000000003\n",
      "[-0.66400105] [20.92] 3161.16\n",
      "[-0.66400105] [-77.33] 3244.15\n",
      "[-0.66400105] [91.71] 3270.6299999999997\n",
      "[-0.66400105] [-83.37] 3233.0\n",
      "[-0.66400105] [12.57] 3300.9500000000003\n",
      "[-0.66400105] [30.6] 3267.27\n",
      "[-0.66400105] [-96.99] 3251.0099999999998\n",
      "[-0.66400105] [22.06] 3275.66\n",
      "[-0.66400105] [9.35] 3225.78\n",
      "[-0.66400105] [-29.39] 3272.0699999999997\n",
      "[-0.66400105] [24.23] 3294.48\n",
      "[-0.66400105] [42.5] 3299.2400000000002\n",
      "[-0.66400105] [-24.26] 3303.24\n",
      "[-0.66400105] [-19.51] 3317.48\n",
      "[-0.66400105] [-40.98] 3233.8700000000003\n",
      "[-0.66400105] [0.63] 3230.57\n",
      "[-0.66400105] [-34.31] 3237.32\n",
      "[-0.66400105] [68.89] 3261.3599999999997\n",
      "[-0.66400105] [-53.29] 3207.01\n",
      "[-0.66400105] [6.76] 3223.91\n",
      "[-0.66400105] [12.09] 3224.7799999999997\n",
      "[-0.66400105] [-22.11] 3161.4\n",
      "[-0.66400105] [5.78] 3241.73\n",
      "[-0.66400105] [54.31] 3208.2300000000005\n",
      "[-0.66400105] [-51.74] 3145.3\n",
      "[-0.66400105] [34.27] 3251.07\n",
      "[-0.66400105] [-0.27] 3143.22\n",
      "[-0.66400105] [-11.53] 3160.94\n",
      "[-0.66400105] [23.73] 3163.44\n",
      "[-0.66400105] [12.03] 3157.36\n",
      "[-0.66400105] [-10.59] 3112.34\n",
      "[-0.66400105] [40.93] 3153.1200000000003\n",
      "[-0.66400105] [-1.37] 3143.3099999999995\n",
      "[-0.66400105] [-36.83] 3087.3\n",
      "[-0.66400105] [-0.21] 3067.25\n",
      "[-0.66400105] [-15.29] 3077.62\n",
      "[-0.66400105] [31.96] 3074.5499999999997\n",
      "[-0.66400105] [23.23] 3073.3\n",
      "[-0.66400105] [-66.41] 2964.87\n",
      "[-0.66400105] [26.76] 3027.52\n",
      "[-0.66400105] [16.52] 2897.58\n",
      "[-0.66400105] [-7.79] 2902.0099999999998\n",
      "[-0.66400105] [15.73] 2849.77\n",
      "[-0.66400105] [-45.05] 2736.25\n",
      "[-0.66400105] [22.31] 2746.18\n",
      "[-0.66400105] [67.39] 2767.12\n",
      "[-0.66400105] [-24.39] 2669.59\n",
      "[-0.66400105] [-4.73] 2770.85\n",
      "[-0.66400105] [38.86] 2678.12\n",
      "[-0.66400105] [-10.5] 2788.8799999999997\n",
      "[-0.66400105] [66.9] 2754.85\n",
      "[-0.66400105] [-64.32] 2771.23\n",
      "[-0.66400105] [5.95] 2736.84\n",
      "[-0.66400105] [40.] 2780.34\n",
      "[-0.66400105] [45.1] 2797.73\n",
      "[-0.66400105] [7.17] 2896.52\n",
      "[-0.66400105] [-8.07] 2805.5299999999997\n",
      "[-0.66400105] [-37.46] 2787.31\n",
      "[-0.66400105] [24.43] 2855.44\n",
      "[-0.66400105] [28.08] 2866.94\n",
      "[-0.66400105] [17.82] 2860.0099999999998\n",
      "[-0.66400105] [-23.74] 2840.6099999999997\n",
      "[-0.66400105] [6.95] 2830.09\n",
      "[-0.66400105] [9.07] 2876.9\n",
      "[-0.66400105] [60.63] 2856.95\n",
      "[-0.66400105] [-75.89] 2831.4800000000005\n",
      "[-0.66400105] [23.19] 2839.02\n",
      "[-0.66400105] [30.74] 2817.63\n",
      "[-0.66400105] [-36.89] 2842.82\n",
      "[-0.66400105] [63.8] 2826.35\n",
      "[-0.66400105] [-23.29] 2842.6699999999996\n",
      "[-0.66400105] [33.15] 2830.5400000000004\n",
      "[-0.66400105] [14.68] 2900.04\n",
      "[-0.66400105] [7.61] 2811.8199999999997\n",
      "[-0.66400105] [-68.43] 2803.5800000000004\n",
      "[-0.66400105] [24.43] 2843.05\n",
      "[-0.66400105] [25.15] 2815.86\n",
      "[-0.66400105] [-13.94] 2856.2099999999996\n",
      "[-0.66400105] [5.03] 2843.25\n",
      "[-0.66400105] [-22.04] 2846.44\n",
      "[-0.66400105] [-2.96] 2747.1\n",
      "[-0.66400105] [5.13] 2928.2799999999997\n",
      "[-0.66400105] [61.07] 2802.07\n",
      "[-0.66400105] [-49.71] 2905.07\n",
      "[-0.66400105] [3.98] 2831.06\n",
      "[-0.66400105] [-1.52] 2869.46\n",
      "[-0.66400105] [-30.57] 2833.06\n",
      "[-0.66400105] [32.06] 2948.75\n",
      "[-0.66400105] [-37.48] 2796.4399999999996\n",
      "[-0.66400105] [88.88] 2990.4500000000003\n",
      "[-0.66400105] [2.04] 3029.23\n",
      "[-0.66400105] [-32.85] 2980.6000000000004\n",
      "[-0.66400105] [-5.66] 2997.47\n",
      "[-0.66400105] [-16.85] 3055.33\n",
      "[-0.66400105] [15.35] 3047.2400000000002\n",
      "[-0.66400105] [-18.59] 3103.19\n",
      "[-0.66400105] [35.57] 3143.12\n",
      "[-0.66400105] [-42.57] 3126.37\n",
      "[-0.66400105] [21.07] 3214.0699999999997\n",
      "[-0.66400105] [18.72] 3188.9500000000003\n",
      "[-0.66400105] [-63.36] 3253.36\n",
      "[-0.66400105] [53.17] 3219.16\n",
      "[-0.66400105] [-64.13] 3207.6900000000005\n",
      "[-0.66400105] [-9.44] 3227.5499999999997\n",
      "[-0.66400105] [24.55] 3207.15\n",
      "[-0.66400105] [-31.44] 3226.2000000000003\n",
      "[-0.66400105] [29.41] 3164.5\n",
      "[-0.66400105] [-34.84] 3232.46\n",
      "[-0.66400105] [67.87] 3152.68\n",
      "[-0.66400105] [-17.61] 3299.96\n",
      "[-0.66400105] [14.21] 3107.0\n",
      "[-0.66400105] [-77.54] 3198.84\n",
      "[-0.66400105] [15.39] 3095.98\n",
      "[-0.66400105] [-4.29] 3126.0899999999997\n",
      "[-0.66400105] [-0.06] 3142.3\n",
      "[-0.66400105] [43.44] 3138.82\n",
      "[-0.66400105] [-26.4] 3062.3199999999997\n",
      "[-0.66400105] [1.74] 3194.9300000000003\n",
      "[-0.66400105] [93.6] 3085.71\n",
      "[-0.66400105] [-49.48] 2986.02\n",
      "[-0.66400105] [6.39] 3199.0299999999997\n",
      "[-0.66400105] [167.72] 3122.91\n",
      "[-0.66400105] [-133.5] 3020.3\n",
      "[-0.66400105] [-61.39] 3007.27\n",
      "[-0.66400105] [91.16] 3037.33\n",
      "[-0.66400105] [-71.34] 2969.18\n",
      "[-0.66400105] [67.83] 2996.37\n",
      "[-0.66400105] [-52.4] 2938.73\n",
      "[-0.66400105] [-7.89] 2905.37\n",
      "[-0.66400105] [-32.65] 2843.08\n",
      "[-0.66400105] [-13.08] 2844.2400000000002\n",
      "[-0.66400105] [44.22] 2837.18\n",
      "[-0.66400105] [-15.87] 2764.17\n",
      "[-0.66400105] [-37.63] 2792.1\n",
      "[-0.66400105] [39.89] 2724.5099999999998\n",
      "[-0.66400105] [-29.01] 2661.6000000000004\n",
      "[-0.66400105] [17.3] 2673.4299999999994\n",
      "[-0.66400105] [42.74] 2673.2700000000004\n",
      "[-0.66400105] [-55.81] 2557.83\n",
      "[-0.66400105] [-24.04] 2533.76\n",
      "[-0.66400105] [23.37] 2521.0899999999997\n",
      "[-0.66400105] [28.73] 2571.0800000000004\n",
      "[-0.66400105] [33.09] 2535.67\n",
      "[-0.66400105] [-114.81] 2451.07\n",
      "[-0.66400105] [41.79] 2492.74\n",
      "[-0.66400105] [38.02] 2457.7099999999996\n",
      "[-0.66400105] [-58.95] 2440.14\n",
      "[-0.66400105] [44.02] 2446.3500000000004\n",
      "[-0.66400105] [13.41] 2452.07\n",
      "[-0.66400105] [-63.24] 2303.6899999999996\n",
      "[-0.66400105] [25.] 2383.19\n",
      "[-0.66400105] [59.71] 2431.04\n",
      "[-0.66400105] [-7.1] 2284.17\n",
      "[-0.66400105] [-47.67] 2314.66\n",
      "[-0.66400105] [10.31] 2298.13\n",
      "[-0.66400105] [21.22] 2271.5099999999998\n",
      "[-0.66400105] [-11.85] 2211.98\n",
      "[-0.66400105] [2.83] 2270.0799999999995\n",
      "[-0.66400105] [19.26] 2171.28\n",
      "[-0.66400105] [-23.64] 2225.25\n",
      "[-0.66400105] [43.55] 2158.82\n",
      "[-0.66400105] [-69.72] 2109.92\n",
      "[-0.66400105] [22.28] 2141.4500000000003\n",
      "[-0.66400105] [20.54] 2074.4199999999996\n",
      "[-0.66400105] [-34.93] 2096.23\n",
      "[-0.66400105] [-7.32] 1989.9099999999999\n",
      "[-0.66400105] [-24.36] 1992.76\n",
      "[-0.66400105] [39.42] 2004.4900000000002\n",
      "[-0.66400105] [0.98] 1976.29\n",
      "[-0.66400105] [7.34] 1932.7099999999998\n",
      "[-0.66400105] [-28.61] 1944.81\n",
      "[-0.66400105] [39.98] 1919.7100000000003\n"
     ]
    }
   ],
   "source": [
    "val_starting_idx = 1+288+2+split_idx # 1 = detrend, 288 = deseasonalize, 2=input seq created nans\n",
    "for i in range(len(y_val_hat)):\n",
    "    extra = dt_data['load'].values[val_starting_idx + i - 288] + data['load'].values[val_starting_idx + i - 1]\n",
    "#     y_val_hat[i] += extra\n",
    "#     y_val[i] += extra\n",
    "    print(y_val_hat[i], y_val[i], extra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## so, the difference values are so large in comparision to the predictions that it makes miniscule change in the y_val, and y_val_hat plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:11:58.791800Z",
     "start_time": "2018-11-23T21:11:58.398456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe53549c710>]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['load'].values[1+288+2+split_idx:])\n",
    "plt.plot(y_val_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:21:50.661820Z",
     "start_time": "2018-11-23T21:21:50.655011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forecasting with the predicted values instead of val set values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-23T21:25:10.325176Z",
     "start_time": "2018-11-23T21:25:10.318473Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_forecasts(model, n_batch, train, test, n_lag, n_seq):\n",
    "    forecasts = list()\n",
    "    X = test[0, 0:n_lag]\n",
    "    for i in range(len(test)):        \n",
    "        X = X.reshape(1, 1, len(X))\n",
    "        # make forecast\n",
    "        forecast = model.predict(X, batch_size=n_batch)[0, 0]\n",
    "        X = np.array([forecast])\n",
    "        # store the forecast\n",
    "        forecasts.append(forecast)\n",
    "    return forecasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Version 2\n",
    "\n",
    "Use last `nlags` days values at time t to predict the load of today's time t load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T08:37:37.432491Z",
     "start_time": "2018-11-24T08:37:37.418222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-24 00:00:00</th>\n",
       "      <td>2520.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 00:05:00</th>\n",
       "      <td>2476.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-24 00:10:00</th>\n",
       "      <td>2479.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-10-24 00:00:00  2520.40\n",
       "2018-10-24 00:05:00  2476.88\n",
       "2018-10-24 00:10:00  2479.99"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T08:42:38.605815Z",
     "start_time": "2018-11-24T08:42:38.000194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe4b6c0f5f8>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.at_time('00:05:00').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:30.465282Z",
     "start_time": "2018-11-24T10:20:30.444230Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(columns=['time'] + list(map(str, range(30))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:33.112969Z",
     "start_time": "2018-11-24T10:20:31.196252Z"
    }
   },
   "outputs": [],
   "source": [
    "for idx, time in enumerate(sorted(set(data.index.time))):\n",
    "    df.loc[idx] = [time.strftime(format='%H:%M:%S')] + list(data.at_time(time)['load'].values)\n",
    "#     data.at_time(time).plot()\n",
    "#     if idx>10: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:33.147185Z",
     "start_time": "2018-11-24T10:20:33.115664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00:00:00</td>\n",
       "      <td>2520.40</td>\n",
       "      <td>2491.14</td>\n",
       "      <td>2487.83</td>\n",
       "      <td>2567.64</td>\n",
       "      <td>2286.14</td>\n",
       "      <td>2313.41</td>\n",
       "      <td>2354.25</td>\n",
       "      <td>2368.88</td>\n",
       "      <td>2335.87</td>\n",
       "      <td>...</td>\n",
       "      <td>1905.57</td>\n",
       "      <td>1794.49</td>\n",
       "      <td>1837.89</td>\n",
       "      <td>2016.32</td>\n",
       "      <td>1899.06</td>\n",
       "      <td>1807.17</td>\n",
       "      <td>1763.10</td>\n",
       "      <td>1864.32</td>\n",
       "      <td>1869.17</td>\n",
       "      <td>1861.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00:05:00</td>\n",
       "      <td>2476.88</td>\n",
       "      <td>2491.30</td>\n",
       "      <td>2457.67</td>\n",
       "      <td>2540.15</td>\n",
       "      <td>2272.77</td>\n",
       "      <td>2285.99</td>\n",
       "      <td>2329.92</td>\n",
       "      <td>2344.27</td>\n",
       "      <td>2325.04</td>\n",
       "      <td>...</td>\n",
       "      <td>1886.34</td>\n",
       "      <td>1795.78</td>\n",
       "      <td>1845.33</td>\n",
       "      <td>1987.13</td>\n",
       "      <td>1881.36</td>\n",
       "      <td>1830.02</td>\n",
       "      <td>1749.68</td>\n",
       "      <td>1856.01</td>\n",
       "      <td>1863.78</td>\n",
       "      <td>1813.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00:10:00</td>\n",
       "      <td>2479.99</td>\n",
       "      <td>2485.60</td>\n",
       "      <td>2436.98</td>\n",
       "      <td>2529.15</td>\n",
       "      <td>2258.46</td>\n",
       "      <td>2258.82</td>\n",
       "      <td>2344.88</td>\n",
       "      <td>2323.11</td>\n",
       "      <td>2304.88</td>\n",
       "      <td>...</td>\n",
       "      <td>1844.60</td>\n",
       "      <td>1756.93</td>\n",
       "      <td>1826.49</td>\n",
       "      <td>1995.60</td>\n",
       "      <td>1863.87</td>\n",
       "      <td>1811.94</td>\n",
       "      <td>1744.79</td>\n",
       "      <td>1830.61</td>\n",
       "      <td>1828.91</td>\n",
       "      <td>1815.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:15:00</td>\n",
       "      <td>2467.38</td>\n",
       "      <td>2460.68</td>\n",
       "      <td>2421.81</td>\n",
       "      <td>2523.26</td>\n",
       "      <td>2259.57</td>\n",
       "      <td>2255.95</td>\n",
       "      <td>2342.85</td>\n",
       "      <td>2316.47</td>\n",
       "      <td>2298.62</td>\n",
       "      <td>...</td>\n",
       "      <td>1872.64</td>\n",
       "      <td>1769.04</td>\n",
       "      <td>1808.62</td>\n",
       "      <td>1983.07</td>\n",
       "      <td>1851.78</td>\n",
       "      <td>1752.79</td>\n",
       "      <td>1731.72</td>\n",
       "      <td>1820.61</td>\n",
       "      <td>1841.75</td>\n",
       "      <td>1804.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00:20:00</td>\n",
       "      <td>2460.86</td>\n",
       "      <td>2449.49</td>\n",
       "      <td>2423.02</td>\n",
       "      <td>2509.79</td>\n",
       "      <td>2238.66</td>\n",
       "      <td>2243.72</td>\n",
       "      <td>2315.55</td>\n",
       "      <td>2309.83</td>\n",
       "      <td>2296.95</td>\n",
       "      <td>...</td>\n",
       "      <td>1820.45</td>\n",
       "      <td>1768.90</td>\n",
       "      <td>1795.87</td>\n",
       "      <td>1967.24</td>\n",
       "      <td>1829.15</td>\n",
       "      <td>1750.78</td>\n",
       "      <td>1712.27</td>\n",
       "      <td>1819.67</td>\n",
       "      <td>1817.94</td>\n",
       "      <td>1795.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time        0        1        2        3        4        5        6  \\\n",
       "0  00:00:00  2520.40  2491.14  2487.83  2567.64  2286.14  2313.41  2354.25   \n",
       "1  00:05:00  2476.88  2491.30  2457.67  2540.15  2272.77  2285.99  2329.92   \n",
       "2  00:10:00  2479.99  2485.60  2436.98  2529.15  2258.46  2258.82  2344.88   \n",
       "3  00:15:00  2467.38  2460.68  2421.81  2523.26  2259.57  2255.95  2342.85   \n",
       "4  00:20:00  2460.86  2449.49  2423.02  2509.79  2238.66  2243.72  2315.55   \n",
       "\n",
       "         7        8   ...          20       21       22       23       24  \\\n",
       "0  2368.88  2335.87   ...     1905.57  1794.49  1837.89  2016.32  1899.06   \n",
       "1  2344.27  2325.04   ...     1886.34  1795.78  1845.33  1987.13  1881.36   \n",
       "2  2323.11  2304.88   ...     1844.60  1756.93  1826.49  1995.60  1863.87   \n",
       "3  2316.47  2298.62   ...     1872.64  1769.04  1808.62  1983.07  1851.78   \n",
       "4  2309.83  2296.95   ...     1820.45  1768.90  1795.87  1967.24  1829.15   \n",
       "\n",
       "        25       26       27       28       29  \n",
       "0  1807.17  1763.10  1864.32  1869.17  1861.12  \n",
       "1  1830.02  1749.68  1856.01  1863.78  1813.69  \n",
       "2  1811.94  1744.79  1830.61  1828.91  1815.72  \n",
       "3  1752.79  1731.72  1820.61  1841.75  1804.82  \n",
       "4  1750.78  1712.27  1819.67  1817.94  1795.77  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:33.153515Z",
     "start_time": "2018-11-24T10:20:33.149363Z"
    }
   },
   "outputs": [],
   "source": [
    "df.index = df['time']\n",
    "df = df.drop('time', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:33.185814Z",
     "start_time": "2018-11-24T10:20:33.155835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00:00</th>\n",
       "      <td>2520.40</td>\n",
       "      <td>2491.14</td>\n",
       "      <td>2487.83</td>\n",
       "      <td>2567.64</td>\n",
       "      <td>2286.14</td>\n",
       "      <td>2313.41</td>\n",
       "      <td>2354.25</td>\n",
       "      <td>2368.88</td>\n",
       "      <td>2335.87</td>\n",
       "      <td>2319.43</td>\n",
       "      <td>...</td>\n",
       "      <td>1905.57</td>\n",
       "      <td>1794.49</td>\n",
       "      <td>1837.89</td>\n",
       "      <td>2016.32</td>\n",
       "      <td>1899.06</td>\n",
       "      <td>1807.17</td>\n",
       "      <td>1763.10</td>\n",
       "      <td>1864.32</td>\n",
       "      <td>1869.17</td>\n",
       "      <td>1861.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:05:00</th>\n",
       "      <td>2476.88</td>\n",
       "      <td>2491.30</td>\n",
       "      <td>2457.67</td>\n",
       "      <td>2540.15</td>\n",
       "      <td>2272.77</td>\n",
       "      <td>2285.99</td>\n",
       "      <td>2329.92</td>\n",
       "      <td>2344.27</td>\n",
       "      <td>2325.04</td>\n",
       "      <td>2300.97</td>\n",
       "      <td>...</td>\n",
       "      <td>1886.34</td>\n",
       "      <td>1795.78</td>\n",
       "      <td>1845.33</td>\n",
       "      <td>1987.13</td>\n",
       "      <td>1881.36</td>\n",
       "      <td>1830.02</td>\n",
       "      <td>1749.68</td>\n",
       "      <td>1856.01</td>\n",
       "      <td>1863.78</td>\n",
       "      <td>1813.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:10:00</th>\n",
       "      <td>2479.99</td>\n",
       "      <td>2485.60</td>\n",
       "      <td>2436.98</td>\n",
       "      <td>2529.15</td>\n",
       "      <td>2258.46</td>\n",
       "      <td>2258.82</td>\n",
       "      <td>2344.88</td>\n",
       "      <td>2323.11</td>\n",
       "      <td>2304.88</td>\n",
       "      <td>2298.15</td>\n",
       "      <td>...</td>\n",
       "      <td>1844.60</td>\n",
       "      <td>1756.93</td>\n",
       "      <td>1826.49</td>\n",
       "      <td>1995.60</td>\n",
       "      <td>1863.87</td>\n",
       "      <td>1811.94</td>\n",
       "      <td>1744.79</td>\n",
       "      <td>1830.61</td>\n",
       "      <td>1828.91</td>\n",
       "      <td>1815.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:15:00</th>\n",
       "      <td>2467.38</td>\n",
       "      <td>2460.68</td>\n",
       "      <td>2421.81</td>\n",
       "      <td>2523.26</td>\n",
       "      <td>2259.57</td>\n",
       "      <td>2255.95</td>\n",
       "      <td>2342.85</td>\n",
       "      <td>2316.47</td>\n",
       "      <td>2298.62</td>\n",
       "      <td>2267.14</td>\n",
       "      <td>...</td>\n",
       "      <td>1872.64</td>\n",
       "      <td>1769.04</td>\n",
       "      <td>1808.62</td>\n",
       "      <td>1983.07</td>\n",
       "      <td>1851.78</td>\n",
       "      <td>1752.79</td>\n",
       "      <td>1731.72</td>\n",
       "      <td>1820.61</td>\n",
       "      <td>1841.75</td>\n",
       "      <td>1804.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:20:00</th>\n",
       "      <td>2460.86</td>\n",
       "      <td>2449.49</td>\n",
       "      <td>2423.02</td>\n",
       "      <td>2509.79</td>\n",
       "      <td>2238.66</td>\n",
       "      <td>2243.72</td>\n",
       "      <td>2315.55</td>\n",
       "      <td>2309.83</td>\n",
       "      <td>2296.95</td>\n",
       "      <td>2257.94</td>\n",
       "      <td>...</td>\n",
       "      <td>1820.45</td>\n",
       "      <td>1768.90</td>\n",
       "      <td>1795.87</td>\n",
       "      <td>1967.24</td>\n",
       "      <td>1829.15</td>\n",
       "      <td>1750.78</td>\n",
       "      <td>1712.27</td>\n",
       "      <td>1819.67</td>\n",
       "      <td>1817.94</td>\n",
       "      <td>1795.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0        1        2        3        4        5        6  \\\n",
       "time                                                                      \n",
       "00:00:00  2520.40  2491.14  2487.83  2567.64  2286.14  2313.41  2354.25   \n",
       "00:05:00  2476.88  2491.30  2457.67  2540.15  2272.77  2285.99  2329.92   \n",
       "00:10:00  2479.99  2485.60  2436.98  2529.15  2258.46  2258.82  2344.88   \n",
       "00:15:00  2467.38  2460.68  2421.81  2523.26  2259.57  2255.95  2342.85   \n",
       "00:20:00  2460.86  2449.49  2423.02  2509.79  2238.66  2243.72  2315.55   \n",
       "\n",
       "                7        8        9   ...          20       21       22  \\\n",
       "time                                  ...                                 \n",
       "00:00:00  2368.88  2335.87  2319.43   ...     1905.57  1794.49  1837.89   \n",
       "00:05:00  2344.27  2325.04  2300.97   ...     1886.34  1795.78  1845.33   \n",
       "00:10:00  2323.11  2304.88  2298.15   ...     1844.60  1756.93  1826.49   \n",
       "00:15:00  2316.47  2298.62  2267.14   ...     1872.64  1769.04  1808.62   \n",
       "00:20:00  2309.83  2296.95  2257.94   ...     1820.45  1768.90  1795.87   \n",
       "\n",
       "               23       24       25       26       27       28       29  \n",
       "time                                                                     \n",
       "00:00:00  2016.32  1899.06  1807.17  1763.10  1864.32  1869.17  1861.12  \n",
       "00:05:00  1987.13  1881.36  1830.02  1749.68  1856.01  1863.78  1813.69  \n",
       "00:10:00  1995.60  1863.87  1811.94  1744.79  1830.61  1828.91  1815.72  \n",
       "00:15:00  1983.07  1851.78  1752.79  1731.72  1820.61  1841.75  1804.82  \n",
       "00:20:00  1967.24  1829.15  1750.78  1712.27  1819.67  1817.94  1795.77  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:34.694587Z",
     "start_time": "2018-11-24T10:20:34.684580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2520.40\n",
       "1     2491.14\n",
       "2     2487.83\n",
       "3     2567.64\n",
       "4     2286.14\n",
       "5     2313.41\n",
       "6     2354.25\n",
       "7     2368.88\n",
       "8     2335.87\n",
       "9     2319.43\n",
       "10    2380.31\n",
       "11    2283.87\n",
       "12    2165.11\n",
       "13    2140.82\n",
       "14    2016.19\n",
       "15    1880.24\n",
       "16    1673.24\n",
       "17    1781.98\n",
       "18    1770.85\n",
       "19    1838.00\n",
       "20    1905.57\n",
       "21    1794.49\n",
       "22    1837.89\n",
       "23    2016.32\n",
       "24    1899.06\n",
       "25    1807.17\n",
       "26    1763.10\n",
       "27    1864.32\n",
       "28    1869.17\n",
       "29    1861.12\n",
       "Name: 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['00:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:36.327387Z",
     "start_time": "2018-11-24T10:20:34.917557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 1 #daily freq\n",
    "\n",
    "result = seasonal_decompose(df.loc['00:00:00'], freq=decompfreq, model='aditive')\n",
    "\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only trend is present not seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:41.748832Z",
     "start_time": "2018-11-24T10:20:41.742839Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_df = df.diff(1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:49.400052Z",
     "start_time": "2018-11-24T10:20:49.347924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-29.26</td>\n",
       "      <td>-3.31</td>\n",
       "      <td>79.81</td>\n",
       "      <td>-281.50</td>\n",
       "      <td>27.27</td>\n",
       "      <td>40.84</td>\n",
       "      <td>14.63</td>\n",
       "      <td>-33.01</td>\n",
       "      <td>-16.44</td>\n",
       "      <td>...</td>\n",
       "      <td>67.57</td>\n",
       "      <td>-111.08</td>\n",
       "      <td>43.40</td>\n",
       "      <td>178.43</td>\n",
       "      <td>-117.26</td>\n",
       "      <td>-91.89</td>\n",
       "      <td>-44.07</td>\n",
       "      <td>101.22</td>\n",
       "      <td>4.85</td>\n",
       "      <td>-8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:05:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.42</td>\n",
       "      <td>-33.63</td>\n",
       "      <td>82.48</td>\n",
       "      <td>-267.38</td>\n",
       "      <td>13.22</td>\n",
       "      <td>43.93</td>\n",
       "      <td>14.35</td>\n",
       "      <td>-19.23</td>\n",
       "      <td>-24.07</td>\n",
       "      <td>...</td>\n",
       "      <td>65.60</td>\n",
       "      <td>-90.56</td>\n",
       "      <td>49.55</td>\n",
       "      <td>141.80</td>\n",
       "      <td>-105.77</td>\n",
       "      <td>-51.34</td>\n",
       "      <td>-80.34</td>\n",
       "      <td>106.33</td>\n",
       "      <td>7.77</td>\n",
       "      <td>-50.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:10:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.61</td>\n",
       "      <td>-48.62</td>\n",
       "      <td>92.17</td>\n",
       "      <td>-270.69</td>\n",
       "      <td>0.36</td>\n",
       "      <td>86.06</td>\n",
       "      <td>-21.77</td>\n",
       "      <td>-18.23</td>\n",
       "      <td>-6.73</td>\n",
       "      <td>...</td>\n",
       "      <td>57.19</td>\n",
       "      <td>-87.67</td>\n",
       "      <td>69.56</td>\n",
       "      <td>169.11</td>\n",
       "      <td>-131.73</td>\n",
       "      <td>-51.93</td>\n",
       "      <td>-67.15</td>\n",
       "      <td>85.82</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:15:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.70</td>\n",
       "      <td>-38.87</td>\n",
       "      <td>101.45</td>\n",
       "      <td>-263.69</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>86.90</td>\n",
       "      <td>-26.38</td>\n",
       "      <td>-17.85</td>\n",
       "      <td>-31.48</td>\n",
       "      <td>...</td>\n",
       "      <td>74.49</td>\n",
       "      <td>-103.60</td>\n",
       "      <td>39.58</td>\n",
       "      <td>174.45</td>\n",
       "      <td>-131.29</td>\n",
       "      <td>-98.99</td>\n",
       "      <td>-21.07</td>\n",
       "      <td>88.89</td>\n",
       "      <td>21.14</td>\n",
       "      <td>-36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:20:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.37</td>\n",
       "      <td>-26.47</td>\n",
       "      <td>86.77</td>\n",
       "      <td>-271.13</td>\n",
       "      <td>5.06</td>\n",
       "      <td>71.83</td>\n",
       "      <td>-5.72</td>\n",
       "      <td>-12.88</td>\n",
       "      <td>-39.01</td>\n",
       "      <td>...</td>\n",
       "      <td>41.88</td>\n",
       "      <td>-51.55</td>\n",
       "      <td>26.97</td>\n",
       "      <td>171.37</td>\n",
       "      <td>-138.09</td>\n",
       "      <td>-78.37</td>\n",
       "      <td>-38.51</td>\n",
       "      <td>107.40</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-22.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1      2       3       4      5      6      7      8      9  \\\n",
       "time                                                                            \n",
       "00:00:00 NaN -29.26  -3.31   79.81 -281.50  27.27  40.84  14.63 -33.01 -16.44   \n",
       "00:05:00 NaN  14.42 -33.63   82.48 -267.38  13.22  43.93  14.35 -19.23 -24.07   \n",
       "00:10:00 NaN   5.61 -48.62   92.17 -270.69   0.36  86.06 -21.77 -18.23  -6.73   \n",
       "00:15:00 NaN  -6.70 -38.87  101.45 -263.69  -3.62  86.90 -26.38 -17.85 -31.48   \n",
       "00:20:00 NaN -11.37 -26.47   86.77 -271.13   5.06  71.83  -5.72 -12.88 -39.01   \n",
       "\n",
       "          ...       20      21     22      23      24     25     26      27  \\\n",
       "time      ...                                                                 \n",
       "00:00:00  ...    67.57 -111.08  43.40  178.43 -117.26 -91.89 -44.07  101.22   \n",
       "00:05:00  ...    65.60  -90.56  49.55  141.80 -105.77 -51.34 -80.34  106.33   \n",
       "00:10:00  ...    57.19  -87.67  69.56  169.11 -131.73 -51.93 -67.15   85.82   \n",
       "00:15:00  ...    74.49 -103.60  39.58  174.45 -131.29 -98.99 -21.07   88.89   \n",
       "00:20:00  ...    41.88  -51.55  26.97  171.37 -138.09 -78.37 -38.51  107.40   \n",
       "\n",
       "             28     29  \n",
       "time                    \n",
       "00:00:00   4.85  -8.05  \n",
       "00:05:00   7.77 -50.09  \n",
       "00:10:00  -1.70 -13.19  \n",
       "00:15:00  21.14 -36.93  \n",
       "00:20:00  -1.73 -22.17  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:20:58.171425Z",
     "start_time": "2018-11-24T10:20:57.045416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 6)\n",
    "decompfreq = 1 #daily freq\n",
    "\n",
    "result = seasonal_decompose(dt_df.loc['00:00:00'].dropna(), freq=decompfreq, model='aditive')\n",
    "\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:21:03.161824Z",
     "start_time": "2018-11-24T10:21:03.153526Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_df = dt_df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:21:27.264345Z",
     "start_time": "2018-11-24T10:21:27.258433Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "scaler = scaler.fit(dt_df.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:21:28.194524Z",
     "start_time": "2018-11-24T10:21:28.188521Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_df = scaler.transform(dt_df)  # df is now a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:21:28.474926Z",
     "start_time": "2018-11-24T10:21:28.470274Z"
    }
   },
   "outputs": [],
   "source": [
    "split_idx = int(len(dt_df) * 0.8)\n",
    "train, val = dt_df[:split_idx, :], dt_df[split_idx:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:21:29.364748Z",
     "start_time": "2018-11-24T10:21:29.358115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((230, 29), (58, 29))"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:07:10.838080Z",
     "start_time": "2018-11-24T10:07:10.830559Z"
    }
   },
   "outputs": [],
   "source": [
    "def prepare_data(data, nlags):\n",
    "    '''prepares data for LSTM model, x=last nlags values, y=(nlags+1)'th value'''\n",
    "    data_x, data_y = [], []\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(0, data.shape[1]-nlags):\n",
    "            data_x.append(data[i, j:j+nlags])\n",
    "            data_y.append(data[i, j+nlags])\n",
    "    data_x = np.array(data_x)\n",
    "    data_y = np.array(data_y).reshape(-1, 1)\n",
    "    return data_x, data_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:07:12.215762Z",
     "start_time": "2018-11-24T10:07:12.203092Z"
    }
   },
   "outputs": [],
   "source": [
    "nlags = 20 \n",
    "train_x, train_y = prepare_data(train, nlags)\n",
    "val_x, val_y = prepare_data(val, nlags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:07:13.347230Z",
     "start_time": "2018-11-24T10:07:13.340551Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2070, 20), (2070, 1), (522, 20), (522, 1))"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape, train_y.shape, val_x.shape, val_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:10:13.537777Z",
     "start_time": "2018-11-24T10:10:13.532621Z"
    }
   },
   "outputs": [],
   "source": [
    "train_x = train_x.reshape(train_x.shape[0], 1, nlags)\n",
    "val_x = val_x.reshape(val_x.shape[0], 1, nlags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:10:15.219259Z",
     "start_time": "2018-11-24T10:10:14.644802Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(1, batch_input_shape=(1, train_x.shape[1], train_x.shape[2]), stateful=True))\n",
    "model.add(Dense(train_y.shape[1]))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:11:31.549878Z",
     "start_time": "2018-11-24T10:10:16.526348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 8s 4ms/step - loss: 0.0251 - val_loss: 0.0080\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 4ms/step - loss: 0.0144 - val_loss: 0.0096\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 4ms/step - loss: 0.0109 - val_loss: 0.0136\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 3ms/step - loss: 0.0088 - val_loss: 0.0117\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 4ms/step - loss: 0.0073 - val_loss: 0.0101\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 4ms/step - loss: 0.0065 - val_loss: 0.0090\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 4ms/step - loss: 0.0061 - val_loss: 0.0078\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 3ms/step - loss: 0.0058 - val_loss: 0.0073\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 7s 4ms/step - loss: 0.0056 - val_loss: 0.0071\n",
      "Train on 2070 samples, validate on 522 samples\n",
      "Epoch 1/1\n",
      "2070/2070 [==============================] - 8s 4ms/step - loss: 0.0055 - val_loss: 0.0072\n"
     ]
    }
   ],
   "source": [
    "# fit network\n",
    "for i in range(10):\n",
    "    model.fit(train_x, train_y, epochs=1, batch_size=1, verbose=1, validation_data=(val_x, val_y), shuffle=False)\n",
    "    model.reset_states()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:16:02.116500Z",
     "start_time": "2018-11-24T10:16:01.444832Z"
    }
   },
   "outputs": [],
   "source": [
    "val_y_pred = model.predict(val_x, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:26:50.959416Z",
     "start_time": "2018-11-24T10:26:50.401447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f155866b2b0>]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(val_y[:100])\n",
    "plt.plot(val_y_pred[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:17:01.569548Z",
     "start_time": "2018-11-24T10:17:01.563914Z"
    }
   },
   "outputs": [],
   "source": [
    "inverted_val_y = scaler.inverse_transform(val_y)\n",
    "inverted_val_y_pred = scaler.inverse_transform(val_y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:28:24.540539Z",
     "start_time": "2018-11-24T10:28:23.973965Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f155861e978>]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAFpCAYAAAAhueFnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXuQZOlZ3vn78nLynnXJrK6+zXT39GVuEtLIGszN6zUYG2xsEcYmsIXFGhti18T6tuFYsxv2mrAXL4vDeAkMsYBsz4CwjZFZAStsg5BWIDGS5iJppkfTPT3T96rurmveT56T53z7x/edrKxL90xVZVaernx/ERNVlVmdeSYr85zv+d7nfV6ltUYQBEEQBEEQBEE4HCTGfQCCIAiCIAiCIAjC8BCRJwiCIAiCIAiCcIgQkScIgiAIgiAIgnCIEJEnCIIgCIIgCIJwiBCRJwiCIAiCIAiCcIgQkScIgiAIgiAIgnCIEJEnCIIgCIIgCIJwiBCRJwiCIAiCIAiCcIgQkScIgiAIgiAIgnCIEJEnCIIgCIIgCIJwiEiN+wDeDdVqVZ8+fXrchyEIgiAIgiAIgjAWXnrppWWt9dy7+d2HQuSdPn2aF198cdyHIQiCIAiCIAiCMBaUUtff7e+KXVMQBEEQBEEQBOEQISJPEARBEARBEAThECEiTxAEQRAEQRAE4RAhIk8QBEEQBEEQBOEQISJPEARBEARBEAThECEiTxAEQRAEQRAE4RAhIk8QBEEQBEEQBOEQISJPEARBEARBEAThELFvkaeUyiqlvqiU+opS6qJS6sfs7WeUUl9QSl1RSv0HpZRjb8/Yn6/Y+0/v9xgEQRAEQRAEQRAEwzAqeV3gW7XW7wPeD3yHUuobgJ8AfkprfQ5YA/66/f2/DqzZ23/K/p4gCIIgCIIgCIIwBPYt8rShaX9M2/808K3Ar9nbnwO+237/Ifsz9v5vU0qp/R6HIAiCIAiCIAiCMKSePKVUUin1ZeAe8DvAW8C61rpnf+UWcMJ+fwK4CWDvrwGVYRyHIAjCvtAali6N+ygEQRAEQRD2xVBEntY60Fq/HzgJfD3wxH4fUyn1w0qpF5VSLy4tLe37GAVBEN6Rq5+Ff/X1sPzmuI9EEARBECaXMIT//KOw8ta4j+ShZajpmlrrdeDTwDcC00qplL3rJHDbfn8beATA3j8FrOzwWD+vtf6g1vqDc3NzwzxMQRCEnWneNV/rC+M9DkEQBEGYZOq34YWfhUu/Pe4jeWgZRrrmnFJq2n6fA74d+BpG7P1F+2s/AHzCfv8b9mfs/b+ntdb7PQ5BEIR947XM1259vMchCIIgCJOM3zZf3fXxHsdDTOqdf+UdOQY8p5RKYkTjr2qtf0sp9Trw75VS/xR4Bfio/f2PAr+klLoCrALfN4RjEARB2D99kdcY73EIgiAIwiTj2UzHjoi8vbJvkae1/irwzA63v43pz9t6uwv8pf0+ryAIwtDp7xxKJU8QBEEQxka06SqVvD0z1J48QRCEh5po51DsmoIgCIIwPiKR11kb73E8xIjIEyaCt5aavHR9ddyHIcQdL6rk1cZ7HIIgCIIwyfRFnlTy9oqIPGEi+L9+903+3q9+ZdyHIcQd6ckTBEEQhPETOWvErrlnROQJE0Gz22Op0R33YQhxx5d0TUEQBEEYO5GzRip5e0ZEnjARdLyAthfQ8YJxH4oQZ/qN3iLyBEEQBGFsDAavyKS1PSEiT5gIOr4RdystqeYJDyDaORS7piAIgiCMj8iuGfY2vhd2hYg8YSJwrchbbXljPhIh1ki6piAIgiCMn6iSB2LZ3CMi8oSJwO1X8kTkCQ9A5uTtzNd+c6PKKQiCIAijZlDkSfjKnhCRJ0wEkV1ztSkiT3gAngSvbGPtOvyH74fXPzHuIxEEQRAmhUGLplTy9oSIPGEiiAJXpCdPeCBRtcprQighPcDGzMD2yniPQxAEQZgc/DYkHfO9VPL2hIg8YSJw/RAQu6bwALQ24i6VNT9L+Iqhn3AmA+IFQRCEA8JrQfm4+b6zNt5jeUgRkScceoJQ4wVG5IldU7gvgQc6gNJR87NYNg2+iDxBEAThgPGaUD5pvhe75p4QkScceqLQFZB0TeEBRBWrkt05lPAVg1TyBEEQhIPGa0FpHlRS7Jp7RESecOjpDIg8sWsK96Uv8qJKntg1gY0+RRF5giAIwkHhtcApQG5aKnl7RESecOiJQldAgleEBxCNTygdM1/FrmmIEs5E5AmCIAgHhdcCpwjZaank7RERecKhx/UD/pvEV/ihwmelJ0+4P5GYiSp5Ytc09O2acpEVBEEQDgCtpZI3BETkCYeejh/wl5O/xw/pj9Pygk09eoLQJ7IlRmleXalcAQMD4uX1EIRd07gLdy+O+ygE4eHC7wDaiLzstKRr7hEReYeQn/3MFf7Ty7fGfRixwfVDCrgUMYtVCV8RdkR68nZGglcEYe985p/Bx7533EchCA8X0XXHKZpKnjhJ9oSIvEPIf3zxFr/+yu1xH0Zs6PgBBeWSC9soQhF5ws5EowLyVUikxK4ZEV1svSYEvfEeiyA8bLSWoLEAoThIhAfQbcJ//Ye2giX02yecAuRmxK65R0TkHULaXk+EzAAdL6CAi0JTxGW5KeErwg70dw4LkClJ8EpE9LqAvCaCsFvcGugQ2qvjPhIhztz4Q/j8T8P1z4/7SOJBdN1J523wSs306Qm7QkTeIaTjBaxIwEgf1w8oKrM7VqItAljYmagnzylApiyVvIhBkSeWGUHYHdHGSOveeI9DiDfR+0R6zwxb7Zo6kBaKPSAi7xDS8QNWWx5adj0Aa9fEBaCsROQJ92HQHpItywUlwh8UedKXJwi7IvrMNEXkCQ8gut60V8Z7HHFh0/V42nwvAnjXiMg7ZPhBiB9ovCCk2ZX+GTCVzbwVeTPJjgxEF3bGb4NKQtKBzJRYEyO8FqSy5nsRecKDeOk5ePXXxn0U8SJyBLSWxnscQrzpWlEjIs/gDzhrclbkiZNk14jIO2R0BsYDiGXT4HkuGWUE7/FslxXpyTNc+xz82g9CGI77SOJBNHhVKdOTJ3ZNg9feGCshIk94EF/8BfjSR8d9FPFB643NIqnkCQ9CKnmbGeyRz82Y7yV8ZdeIyDtkdLwBkScVKwDCzsZifd7xxa4Z8fZn4LWPy+5YhNeil8rxZ3/69+mmCjInL8Jr0ivYsRJykRUeRLcO7eVxH0V88NsQWkeN9OQJD8KTSt4m+nbN4oZdU9Yqu0ZE3iFjUOSJmDGEkQ0COOJ0RfxGRHaIlizKAPBauCrLxYU6y35WevIsodfm/71uLxVSyRMehNeU88kgg26Aptg1hQcQVXxF5Bk2VfKinjwRebtFRN4hoz1YyRNbIrBZ5FVTHRG/EdFJVHpFDH4bP5EDoBbmzAJNwovAa3IvLBOSEJF3QLx0fY2F9YdwXla3YcIRZCacYbCvVyp5woPo9+TJqA1g+wgFkOCVPSAi75DR8TfCVqRiZRkQeTMJl1XpVTRElTyxVxm8Fp4VeSu9jIlsjl6jSSUMSfQ6tMjSTRVF5B0QP/Kxl/nn/+XSuA9jd/S6EHiAlsVYRFTJS6SlJ2+Am6tt/tWnr0gC+CCRc0Qq4QavZQReImGqeYmU2DX3gIi8Q0bH2wjRkIqVQXkbIm860abR7dHtyU7zRiVPLioAeC26CZMiueRnzG2THr5iRW5LZ2krEXmbWLu2eYbgEKm7Pl+785DZhQc20+ScYok+L7OPiWNigE++ushP/pdL3Fid8E20AbQVebq9Ig4SsEFoBfO9UqaaJ3bNXSMi75DR9gYqeWLXBCDpbyw+ipiLighgpCdvK7YnD2DRdcxtk96XZ0VMhwwNVRCRN8gvfCt89p8P/WG11nT8gLfuNekFD1Hy7aA1UdwBhii8qXLOVPIkyRiA+cXf43OZ/5Grd6T/LKLVNO8VFfpy3YHNIg9MwqZU8naNiLxDRjRCoZRNiV3ToiIxk69S0GbRKuMl2KhCyILM4LfpYETeQidlbpv0WXl2EHpLZ6npvIi8CL9jAhLuXhz6Q7t+iNbgBSHXH6ZKx+DCVDaODJEToHLW2L/FxgrAdOMyJ9QKC7euj/tQYkPCaxBoZX6Q8JX+SKO21zO23pxU8vaCiLxDRpSueXImL0LGkuxZMTN1gmxgqnpSycPMPwNZkEV4LToYm+aNdtrcNumixm4EtMmwGojI6xMt1leuDP+hB2adXn6YLJsDtnhZpFqiTaLKOfNVwlcASNj3ytK9u2M+kviQ9FssUjE/SPgKeE2CVJ5n/+nv8slX71i7pmyS7BYReYeMdl/k5UTIWNKRyCsdx+mZRZO8NvSrNNIrYvFatG0l754X2TUnvJLXF3lZVnpZsctERDvKa9cg8If60IOW+0t3HyKRN1jJE5FncGugkjB7xvws4SsApH3zXllbEZEXkfKb3NJz5gf5/NggtCwtL+DlG2umkifXn10jIu+QEe0CRyJP0qsgFbQJSEBxjpS9uCxLv+JGJU8uKBD0IOjSDE0lr6Hz5vZJ742IRJ7OsBrm0VLJM0Q7yjqAteFazgZnnb55t/mA34wZYtfcjluHTAmK8+Zn2VADINUz7+vW+pKsUQB6XZLa50Z4xPws12TwWvRS5jp8dbklwSt7RETeIaPjBSQUHJvK4gUhjW7vnf/RIScTtOkm8pCdQnXrpBJKKnkgwSuD2KpmU2dIKGhiRilMfLrmQCWvrvOmv7Unn51NO8pDtmxGboxMKvFQVvJ0Oi99vhHdOmSnoGArNFLJA8CxIs/xa5IdAP1k2hvaiLxeUzYD8Ft4CSPy3l5q2uCVmoQX7RIReYeMjh+Qd1JUCqYiITPhIBO28ZIFI/J6LnN5EXloje4Hr6zIidNWNRuhw8mZ/IbIO0C75kqzy6u3YlYpi0YokKVOVN2ccOELm3tDRiTy3nNiiqvLrYdn3IsVeQtqXioREW4NsmWzQE2kpSfPkgnMtWeKJm/de4iq1aPCM5+dO8zi6yT1VbGxDs6tvbnWoZeZArRcf3bJvkWeUuoRpdSnlVKvK6UuKqX+tr19Vin1O0qpN+3XGXu7Ukr9tFLqilLqq0qpD+z3GIQN2l5ANp1ktmh6imSXzIg8P5WHzBQAj+Z78roEHkoHLOmysZxNutfdCt5G6HCqkkerBN1k4UAreT/z6St8/0e/cGDP966wAQkdnaGubZy1WDY3RF4qO3SR51rL/ftOThOE2liVHga6DUIUN4IqtETkAdauOWXmfBXmQCo0gLkmA0yrFleWRORFGyQNnWONEu112QzAa+HaubVBqFkJ7MarhK/simFU8nrA/6S1fgr4BuBHlFJPAf8A+JTW+jzwKfszwHcC5+1/Pwz83BCOQbB0vB55J0nVVvImfVZeEGpy2iVIGrsmwImcN/GvSyRqbll7yMRbNq1ds9ZzKGfTHCll6Kj8gfbk3VhpU+v4m3qyxo6tcGqnsFHJm/QNATC9ISoB8+8ZWSXvfY+Y89WlhyVh02vSJseCX0SLXdPQrZtKHkBxTip5lnxozreVRIu37j0kmxijxNo1g3SRVV3Cb0z4ZkDQg56Lq3L9mxa7RvDJ9Wd37Fvkaa0XtdYv2+8bwNeAE8CHgOfsrz0HfLf9/kPA89rwAjCtlDq23+MQDMauuVHJm3RbousHFJRLL13oX2yPZr2Jf10iG94tXTU/T/qizIreWuCQc5Icm8rRJL8xzPgAWKy5AKy0YrQBYV+X6vQ0NankbdBZM0EA1Quw8tZQHzpK13zPiSmSCcXlh6Uvr1unRZZ7YdFsGkmghqnk2c1FCkekJ8+S0+b6czzjSiUP+puJTqFMI1GWEQp209W1adcwMNZIwld2xVB78pRSp4FngC8A81rrRXvXHcDGS3ECuDnwz27Z24QhENk1KwWxa4IRvQU6hOlC/2J7NN2d+NclqtD0I5snPfXNvh5rfpq8k+TYVJZamD1Qu+ZirQPEbGPGa+KSoVLO0k0VzW0i8ozIy82YIdeNhf5O/FAe2g/48dQvcOTt/8SZaoHLD0vCZrdBQ+dY1SVU6EvvDJjPSiaq5B2R8yyA1hQw59u5VFt68qDfkxemS/iZGZzuhIs8u7nYsiIvk0pwpWHHGkklb1cMTeQppYrAx4G/o7XedHbXJiN3V9t6SqkfVkq9qJR6cWlJTozvFtdW8rLpJAUnOfED0V0/oIhLmC72L7aVtEvD7eH1DiZs5JOvLvLGnZgteGyv1U2xaxrs67Hmb1TyVoMc+oAWqh0vYK1t5q3FagPCb9NROXLpFMn8jLlNRJ5ZaOSmN4Zcr749tIfueAHflXyB/OVP8Ph86aGp5Oluk6bOsqqtqJn08JUw3GzXLMwZkTfpFU6vRdIuB2dUk9vrnU2zISeSqC3AKUK+Qr434edYu+na0qbt6IljZS7VkuY+qeTtiqGIPKVUGiPwPqa1/k/25ruRDdN+jXwKt4FHBv75SXvbJrTWP6+1/qDW+oNzc3PDOMyJoO0F5NLmwzBbdFiNk/VrDLh+QF65aGfDrjmbNBWTtfbBLKb/51/7Kh/9/asH8lzvmm12zYNdkL2+UOe12zG6kNnXoxamyadTHJvKsh5mCQ+okhdV8SBmibheiw4ZCpkkmaIVeXKRHajkWZE3xL68dteniEti+RLn54vcWG3Hq0/zPoRunYbOsULJ3DDp4SteE9Abds3iEQi8ia9E+B1z3g9JUAiNuHl7acL78iInQKZEulSlrBu03Qleu9lN16bOkE4qHp8v8tqqMvdN+OdntwwjXVMBHwW+prX+FwN3/QbwA/b7HwA+MXD7R2zK5jcAtQFbp7BPOl5AzrEir5CJV1VgDHS8kCIuOKX+xXYmYRbUBzEQveMFNLq9eFnwoL9TVtcFarqAPuBekR/99Vf5R5947UCf84H0h35nyTkJjk1naegcunNQIs/tfx+r94rXoqUz5J0UhUKJHkmp5AF01lnXRdZzJ83PQ+zLC906CaVRtZs8VUmiNVx5CCxt2m3QJDdQyZtwd0D0OYnsmgXrmpjwhE23YRbprewRHK8GaN6a9L48K2oSmQL56XmSSnNz4c6YD2qM2OtxM8ySSyd5bK7I7RbopCPpmrtkGJW8bwb+KvCtSqkv2//+DPB/AN+ulHoT+JP2Z4BPAm8DV4BfAP7mEI5BsETBKwDVgiN2za5LRvkkMkUj9FCUbD/AQSymIyEZO7HtR0OuM6zoEm7t4ESeH4R8bbHOcpzem97G65FzUhybytEgj/IORuQtrG9U8mL1XumLvCSVUpYGBRF5AJ01fvutDj/1mVtQPjnUSp4eeH2fdMy8rIdiKLrXoEWO1X4lb8JFXmT1HkzXBGhO9gw0r2VEXjt/kkToUVBd6cvrNmiTJZtxmKqY+Io7d26N+aDGiL0e10OHvJPiTLUAKHrOlDhJdklqvw+gtf4DQN3n7m/b4fc18CP7fV5hZzbZNQsOFxdi1gt2wHgdszhSmSIkEpApU9AHJ/KWrMg7KGvou8ZW8rL5Eqt+mdn6PXLv8E+GxVtLTbxeyFrMxIxG4eKQT5vglYbOkwy60PMg5Yz06aNK3kw+HSuLtfZaNHWGgpMklVTUdJ5pt3bfE/5EEIZot8a9Xp4Xr6+Z8JWhiryNc/Zx7xpOcoY3HwKRp7pNG7wiPXnARmjT1krehI9RiESeWzgJqy/y9EwoCZtds0GSd5JUjxwHYPneBBvcbGWzFjjknSRn50yys5sskRa75q4YarqmMH6MXdNod9OT56EnuNHbb5sLbTJrd5ezZXK2D+AgqpxLDbNgj1WfFfR70E4dq7KiywdqIbp42/xNGt0e3V5Meo38NjqVQ5Mg7yQ5UsqYEQpwILPyFmsdKgWHo1O5WNk1tdeirTPkMykqBYeazhO0J/wi262h0NR0kTfuNOjNnIWVN4cWqJEYqB4nV97k7JFi/Ct5WpPwjV1zZmoaF0fsmv1K3rT5WrQB4xNu1/TbplLtlUw0w1PTwUNhRx4p3QZNnSXvpMhOmYpvfUXsmrWeQz6T5JHZPAkFdVWUSt4uEZF3iOgFIV4Q9it51UIGLwhpdCc3uSoKzkjmIpE3hdNrkkyog6nkWZHX6B5cmue7wp5ET8xVWVdlUu7BRTYPVpfXbaLk2PGaBGkj6nJOklQyQSKyWR3ArLyFdZdj01kqBSdWNlbdbdIia+yahQx1nafXnvCeCNsTUtMFglBzO3nCWFiHNNsq0d9UULB0icfni1yO+0B0v4PSIS2d5dx8iRVdImgenMjzeiHf83Of59OXYlQli2y30XkkNwMqOfGVvMAGrwTlRwE4X+5xbblNL4jR9fGA0d0mdZ0jm05C3oShdWoTvBlgN6HXeyYILZMyQm81zEvwyi4RkXeI6PimKpJ3NuyaEMMq0gESWLtmOmcvtJkyqttgJu8cyNDpSORBvCyboRV5mVyBMFch11s3kd8HwMWFDdEUm6qV16aXtCLPbpKkizYV7wASNhdrHY5N5ajY6nts8Nu07Q5zpehQo4DuTHhPnhV56xgL0cWu7bUakmUz6VtBN/80LL3B+fkSCzWXhhuTDZGdsMK0SY7zR4qs6hJu7eB6zy7fbfDS9TVeeCtGFtGtwSuJhBmjMOED0YPofDpjRN6ZfBcvCLm51nnAvzrchG6dljabaeQrAPQaEyzyopFGPacfJPhYtcA9PyfBK7tERN4hIhJ5T9/7LXjzd5gtykD0wEYTO/mNSh7uOpUDCqVZGkjwjNPivecaG14hmyZZOkKS8EB2yLTWvL5Y73vsY9OX57fpJU1XYt7anXPRyIADmJW3uO5yfCrLbCFeIk95LdqYnryokqcOoLIZa6xdqKbNe/gP1+37ZEgiLx2JvJPPwtpVnqiaWVGxHopuRV5D5zh3pMiaLhE0D05wRRtHd+ruO/zmAbI1eAVM+MqED0SPek4T08aueSJr/maTHL6iu8bqnHeS4OTxE1ky/jq1Tow3dkaJ1wKVpOYl+kWLM9UiC24GLXbNXSEi7xARzVJ679WPwhd/nmrBLA5WDmBUQFzRdvHh5G1VJlsGt35gi+nlRlxFXpM2Jho/N216RfzG6HeYb611aLg9/th5U/1YjUt102viW5EX7RwWymZHVY+4ktdwfRrdHsemc1QKDs249CqGIYlemzZZck6SStGhToHUASWOxpZ+Ja/I0XKWTy1mIJEamshzegMiT4c8nTGfy1gPRffMsbXIcX6+yAplEgfYkxdZwO/UYiTy3DokHX73co1/8TuXzW2FIxNfycOt09RZnLIJoplPmwreRIevWJEXuUh6mRlmaXBteULnB3otcIq0/XCjkjdXYDXMo7p1CGNwfXxIEJG3R1w/4NVbNdpefPrd2lbkpUIXOuv9Sl6cxMWB06/kbdg1cWv9UJpRs9TsMl82YjtOf4ew2zSVvEyScuUoAEt3F0b+vNGO+7ecM30HsankeW26iaiSZy4qU9OmQtNpjHbnMFqYHpvKMluI0XulZxZf5n2SYrbgUNd5UmEX/Bgtpg+afk9ekW8+V2Wh0aM3dXpoIi/Ta9FTaTj2PgCOdK+RSyfjLfIG7JqnKwXWdBmne3C2qtdum/PK3ThV8twaZMr81quL/MzvvUmz2zMD0Se8kqe8Bg3y5PJFSGbI+jXmSpmJruThNWnqXN9FogoVZlSDqxMr8prgFDaNBHusWmDduidkjM+7R0TeHvni1VX+3M/8Aa/eis+bLbJrpgK3b0mEybZrJuw8OJUZsGt261Tz6QN5XZYaXR4/agRmLBbulqDboo3ptZo7egKAlbujn8tzcaFOMqH4hrOmSrbaiokdxWvhqSywIfJmZm3KWW20trMFK/KOT+f6fbSxmG9p+zaj4JVsOkk3ZT9HB2BhjS3W1lyjwLecN+/jlewjQxuIng2auMkCVM6BSpBYvsyF+eJDIfIaOkcpm8bLzOCE7QPZDAhCzdcWzfMv1tz4pEl365At0+z2CDW8cmNtoycvLsc4BhLdBk2dI5dJQX4WOmucmytOdCUv4W0EXAGkS1VmD1DkXbnX4GNfuH4gz/Wu8Frg5Gl1exSs8H1srti3yB9I+MriV+D5Dw3tvD4uROTtETOckVjttER2zWSvDZ01sukkBScZjwXjmFB2oYpjTw7ZMuiQ+WxArePjjzDRS2vNUqPL+SNFlIqXyMNr0SFDwUlx7LhpgK8tj34uz8UF049XzKQoZ1PxmQnnt3CtyMtG6bRVU21s10ebPLpoB6Efm8pSiVP1PRoQrzP9HeZ+f9Ek76R21ukls3ik+eCpWZxUgqvhUVh9ayjhRdmwhZcsQjoLM6dh6Q0uzJe4dCfGi2DrmHBVDieVQBWM+D2IWXlXl5t0/IAnj5Xp9sL49DG5dchO0XCN2+dLV1dNJS/oTvQmSdJv0iBHPp00iaOdNc4eKfDWvWZ8BPpB0uuSCD0aOte3JiYLVY4km1xbOZj15XOfv87/+uuvxScB3GujnQLd3oZdc76cwU3Z689BhK+8/DzceMFsRDzEiMjbI8enzcUsbiIvQUgi9Ew4gNbWlhiThfQYSPhNAhKQtqO+s6Y3bz5rh5SPcDFteqtCjpazTOfS8Vi4R3htO/8syXTF9OS5tdH3ilxcqPH0cfM3mC04rMZmhEKLjsqSUJBJmdPi0dkyrk7jNke7a7hQc1EK5svZjUTcOLxXIpFHloK90PZnfk24yHNT5j08nU/znuNlvtKpQs+F+u19PXQQagq6hZe2FdO5J2DpEhfmSyw3u/F4X+yEFS2BUwQgVbKDvw+gL+81O3fz2580zxmb8BVr12zaEUZfvLa6MRB9gmflpXpNWuRJJRNW5K1zbq5I3e1tCiqbGOwGSTQMHYB8hRkOrpIXPc9yXF5/r0WYMhvz0WuilKIwZTePRh2+4rXhq/8RnvqQeY8+xIjI2yPJhOJMpcDbMRJ5bT8gi10EhD54LWYLmYm2ayZ7LTrkQClzg42znkuZhcAoX5tofEK15DBTcOITMgImGt9W8lQqQ1MVCUYcvLLc7HK33uXp4+ZvMFNwYtWT17EVK2XfK0dKWZrk8FujFTSL6x2OlDKkk4l4WaztrKI2Zhg6QCpvL3iTPKuos0YnaURY3knxzKMz/MGaFb+XHirbAAAgAElEQVT77Mtz/YCS6tCLRF71Aqxc4cIRs0kVW8umjTzX9rizU0bMHMSsvNdu18ikEnyT7fONTfjKgF0T4Ms31/Fz5hgneVZeutekkzDjajYqeWZz4K178VlPHRj90KLsJpFX0C1uLtcOpLoZibx7jbiIvCa9VDS3NtW/ebpiN0lGff15/RNmPu4HfmC0z3MAiMjbB2eqhZhV8nrkGfiQuutUD2hUQFxJ9dp9Gx7Qr+RVrMgb5c54JPLmimbIdZzmFSZ6bTpk+heVdnqaRGe01qooAe8pK/Jm8zEZFxCG4Ldo2RTJiGRC0U4UCEdctVqsuRybMov4cjZNMqHikYhrF+5tnemnvmWKUsmjs0Y7WSKbTpBMKJ55dJpLvgkv2q/Ia3sBZVoEg5W80OepjPlsvhlXkddtEJAg6Zj3cWnWvB6NlYOxgD9xrMyJafPcsQlfceuQmaLp9qgWHVw/5ErbOkomOGHT6bVwE7Z9IjcNnVXOWZE3kX15A/2sfUFjLYIpd33kG36uH7BQMy0D9+Ly2fFaGyON0hvX5OqcOa/4rRHbNV9+HmbPwqlvGu3zHAAi8vbBmbkC11da9EbY17UbOl5AVg0sDjtrsZu7ddCkgxZutGsIfZE3kzAntVHaEyLryVwpw0zeidUw9GSv3U9NBOhlZ8n76yNNi42SNZ8+tmHXjMVrYlMkW3pD9EZ4yaKJbB4hC7UOx6fNRkQioeLzmbV2zV4qTzJhqpuZkrHLhJM8EN1dp5koUbSfnfc/Ms09ps0Ijn026Xc8U8kLowHac48DUO28TSmb4lKMRZ6bKJgwDWDmyDEAGqujHYiutea1hRrvOV5mvmw+Q4uxquRN0ej2+OMXTAXixWW7iJ/ghM1s0MRLRiLPVPKOlo0lfCITNgftmumNSh7AjBr9GIUbq+1+DlBs7LJeCz9p1m2D1+TjR43IW18d4edn6TLc+Dx84CMbDrCHGBF5++BMtYAfaG7b4IRx0/YDcgwsDjvrVIoZVlveZDY0A07QpruDyCsr8zc7kEpeKUOl6MTDgmdJ9jqbKnmJwhwVVefacntkz3lxoc7JmRxT+TRAX8yM/b1pxUwz3KhYRfScEil/dAsPrTWL6xuVPIBKISbvFc++F9KF/k1RT0S3OdowmljTWaNBsR9Gc2I6x1wpy93Uyf1X8vweJdp9WznVCwCo5cs8Pl/iclzDV7pN2irfP5/MHzlKTyfo1EYr8m6umrmbTx+fwkklqBadeFTygp6xnDlFvF7ImWqe05U8n72lQSWgOdrXJbaEAVnd2Ujpzc1Az0X1XM4eKfLWJFbyrGOiORC8Eom8WdUYeUvQoBvtXj0+Is9L2LTrzIZd89T8LK5O01wbYSX8lefN3NP3/5XRPccBIiJvHzxmEzbj0pfX8QIKauACZ8coeEFIoxufeX4HSSbo4CUHRJ5dPOV1i8SIEy+Xm11SCcV0Lm0qeXEQNABakwqNyItCRpypI1RUfaT249cX6v1+PDA9ed1e2B/9MTb6Is/ZZNcEwCnhBKNLfat1fDp+wLGpDUtxfCp5ts/K2fj8zJRLdHUKtzHZIm+d4qZAgGcemeZyMG8SNvfz0K5HUbkbKaaZIkw9YsJXjpa4fK8Rj3PIVrp1a3c2C7LjMwXWKNJrjLZiFbkD3nPCvF7z5Ww8evJs9b+bNDbEYibFs6dnefFGDZ2vTK5d055TeqmokmeTCztrnJ0rTmglz7xX2irbvx5HIq+qmiOv5EWPn3eS8ejJ0xq8Ju6WubVgCis1CnTqI2ot6Xnw5X8Hj3+nScI9BIjI2wf9MQpL8RF5U6kBMWftmkCs+sEOkozu4Ke2V/IS3Roz+dFWTJYaXSpFp2/B64WauhsDsR14JHVAL5nvh4wUZ4+aNK+l0VgTm90eV5db/WRNMD15EIMkSSvyaoGzza6ZyE1RpM36iFJAF9ajQegblbzYiDwbvKJsYiLAbDFDnTx+8+AGXccKvwM9l5rO9+2aAM88OsOr7hx67bpZKOwRz4b8qOzG54TqBTNG4UiR9bbfdwjEim5jk92smElRU1PQGm3wymsLNVIJxYV5Uxk6Ws5yJw7VCLtwd21ATzGb5tnTs6y1fbxsdXLtmq5NYU3bc0qUXNhZ49yRIgs1l9akbUhbu2aQLvWvxxRMQM/Zojvy3Ieryy0qBYdHZ/MsNWKwQRJ4oANcZa6Jg+6aUjZNSxXpjaon79InTSLwB/670Tz+GBCRtw9mCw5TuXRswlfafsB0amAx2lnvz91amdAxCtmwTW+wkpfOQtIBt06lONowlKVGl7lSBqAvtmORJtnvtdoQFk55npQKuXP3zkie8muL5uK+tZIHMRB5VszUwwy5dGrTXenCNCU6I+vzWbQN78emNyp5lYITk+AV8z5JDFTyKoUMdV2g157QdE0b3b0aFjbZiN7/yDRXw2MoHcD63ocK+/Z1TeYHRN7cE7D8JhfmzaZiLPvyvCZ1ndu0SdJKTZNyR1vxfe12nXNHiv3ZlvNTWe7UYtA+YYOJWsp8doqZFM+eMVWrVaYnt5JnQ0YCZ8CuCbaSZ97fE2fZjF6TAVt8VOE8lTsYkXemWuBIORuPDSR73XGxds0tG6/d9BRqVMFfLz8H5ZNw9k+M5vHHgIi8faCUilXCpusFlDeJvDUqBSMyJjVhM0+H3uDJE4xl060xW3BGKn6Xml3miptFXix6rayoCQcrnHmzc7g+ooHoF2/b0JXBSl7B9OaNXeRZC1Gtl952QckWpympDovro/mML1jxeHxTJS9D3e3hjzvQyWvSJUMu6/RvqhYdGuQnd4SC/f9eDgobswOBrzs5xXX2n7AZiedUblDkPQ69Dk9mzX2X78ZwEdxt0NBZ8pmN18R3Zsj6o3ufaK25uFDjPSc2Xquj5SxrbR933BZwW7FqYc6xpWyK05U81WKG235xckco2ApnuIPIixI2J07k2euPcgbWKSkHMmVOOG2ur7QJw9FZtK+ttDhdLXCklImHXTNKdSaya27eeNXZadL+CETe2nV469PwzPdDIvnOv/+QICJvnzxWLfB2TE5KbS+gnBwQee46s8WYVEvGRF53+kM1+2SnoFunMuIZgvGt5FmRlx4QeQXTA9BaG00gwMWFOpWCw3w5079txto1x56waV+PtR1EXrFsdlSXV0bTA3Cn1iGVUP33CdD/zI79veK1cVVm00V2puBQ1/mRJ47Glo6xCS33sv1kWoBCJkVq7rz5YR8iL7SVwnRhYACvTdicaV+lUnC4fCeGlbxug1qQ3fRe0fkKpXB0Kaz3Gl2Wmx7vGXAHHLW9rWMPkLCfjwYblTylFM+enuFyK2+Gocext3LU2KpVP1goEnntVR6dLZBMKK5MWl9et4GrsmQcZ/Pt+Vnmki06fsDdEdkoW90ed+tdzlQLzJUyLDW6IxWU7wpbyWthromDG0cAqcIMRd0c/vXxyx8zX5/5/uE+7pgRkbdPzlQLLNRcOt6Ydw4xds1SJPKyU7aSF6MK0gET9nyyyifcWsnLblTyRiV+w1Cz0vSobqnkxUJs++YkqjeJvDkAMt3VkYiLiwt1njpe3ug5YPA1GU2/27vGXlTWfKdv+4ooWJG3tjaa3qLFdZf5crY/ogDof2aXx11991q0Bwf0Aulkgk6ySMqbbJF3189vquQBnDv1CKuU0Mt7F3m6Y15XpzC9caNN2GTpEhfmS7G0a+puk1qY3dQ/kyhWmdJN6u3RLFBfi9wBWyp5AHfGnbBpK3m1SORljfh99vQs19y8GdviTZiYgb6NVWW3V/KcVIJTlfzIB6JrreMVXtRt0FH57aFf+QozmPfRqNxi11bM456xlbxeqGOw6WqD0LQVeVuuydlShSlaww08DAN45Zfh3LfB9CPDe9wYICJvn5yxPvLowzJOXC+gmLAf0NJx6KyTTScpOMmJtGu6bbsQzRQ335Gd6vfkrbf9kcw5XO/49EK9rZK3Ou4TKPQrVyq93a5ZUfWhp8V6vZA37zX6Q9AjosHfY69YWdG76qd2CF4xx9xYG00lb6HW2ZSsCRsib+wbAn6Lts5ss8t002UyvfgJjQPBVtru+rlNPXkAzzw6zdvhMTp3Lu354bWtAPWHzoMZjFych6VLPH60xJt3Y5awGYYozwavDHx+MlPzJJTmzp2FkTztxYU6SsGTx7ZX8sYv8oyYWQ/scPjMhshb1laUTmBfXuia84aK0mOdAiTS/c2Ts3PFkQ5Ed/2Aj/zrL/JDz780sufYNd0GbZXbdu0hX6EQmPfRyESeHZl0ulLgSMl8dsY+Ky8aKRFmcJIJUsnNMqU4XaWkOly7N0SXwJVPQf22mY1n8YOQT766GK9z7R4QkbdP+gmbMejLa/s9CglbFSkf7584Z4sOqxMYvNJt2pNA5P+PsD150WJ6bQTJiYMz8sD4yrPpxPgX7rCRmjgofqO5PAx/jMLluw38QG/qxwMz+Hsmnx6/8LU7h41w+zD0yFbUrI+mt2ix5nJs2vbjBT3oNuMTluS1aOrMtopV6JTJBhNYhYD+OXUl2JyuCfCBR6e5Gh5F7WMgemSD3VTJA1PNW77E+fkiLS+IzWxWoL8oa2wJXinMmAjy1Xuj6fN97XaNM9XCpr9DNBB97OEr9u+4FpjjiSp5Tx4r0UzZ6tUEJmx6UbBQ1HOqVH8gOsC5I0Wur7RG0o8chJq/96tf5vffXOal6zEaAeM1aZHbFvpFvkK6u0omlRjZGIWry+aze7qa769Vxm51tpvQjZ1GGgFTs8Z1tHBniCFxLz9n3EwXvhMw1d7/7Tcu8jc/9jJfvvlw95+LyNsncRJ5Zk5eF5IZs2i3IQGj7j2LK922tYbsVMnr1pmNQmlGsJjui7ziQK9VPibR+FbUJDMDlbyUg85OMZeo90/8w+L1he3JmhHR/MCxYi8q7YE5X32syHNHMPxba81izeV4VMn7/34Cfu6b+u/Lcb9XtNemobcLX52ZwsE34wQmjc4aWiVosH3n/bFqkdupE+S69/qx6Lsl0Y0qHZs3RJh7wlTybDjF5ThZNqOdd3KbPj/T1WMA1FZGFOa0UN+2cVTOmmr8ndqYF6puDdJ56p4ioTZi4FPJBHPHHjW/M4GVvKBdI9CKdG5g4zU/u6mS5weaG6vtoT6v1pof+82LfPLVOzxxtMRa26fujrlNIKLboKl3ruSp9iqnK6ML97u63OZo2fTSHolE3rjDV+z6pB7ssOkKJPOmhWJpaUj5AY27cOm34X1/2QTeAL/0wnV+5Qs3+B/+27M88+jMOzxAvBGRt0/yTopjU9lYJEJ1vIC88iCd27Q7ZiLZYyAuDhi/YxdMO9o1ayOdIbjc3FzJg6iiGoO/g63kJbe8Lipf5WSmPfQLysWFGnknyZlKYdt9M3GYCee30MksIYntFxVrK/LbtaHbNlZaHl4v3LBrvv1pWL/ONC0SKgYir9uko7PbbIkqZ6tMo4qxjjPuOmFmCk2CwpYNgURCoSrnzA97HIqe9Ou4OJDKbL5j7nHo1rlQMJ/NWCVsWmHa0pv7N6cqRuSNIsxpreVxe72zKXQFTOL10XKWu+O2a3brkCnT7Pb6oSsRZ06fAaCzNhrxG2eCTp0muc0W8C2VPGDoQ9F/9jNv8fwfXueH/tgZ/va3mYCkGyvDFZJ7pts0ybTbRN4s+G0uzCZH2pN3upqHoMeRhPkbjH2Mgt00Wg92ruRhrz/rK0OqhH/lV0AHfavm564s82O/+Tp/8skj/P0/9fhwnmOMiMgbAnEZo9D2A/J0jc89N22av8MgPsOVD5ieFXnJ7Ba7ZnYK/DaVnLnwjqLKGZ0oq6XNaZJx+Dtou1OWym4RXYUqR1Mt3l4atsir8+SxMomBcJGIWFQ3vRZhevvgVaBfycuFraHbehejQejTOeh1YfErACRq15nJO2OvvmuvSYvtds20tRIGkzgrr7NG4JjqUWGL+AWYOfkkAN27l/f08Gm/0Y/d34RN2Cw33uJoORuvhE0r8hrkNi3KEkVjq/Lqw69YXbTugMHxCXgt0Jr5cjYePXlZI/JK2fSmu54+9xihVtxZuDGmgxsfoVujQX6zoMnN9Htdo1l5w+zL+9Uv3eQn/8slvvv9x/nR73ySRyvm8zXsauGe6daph9kdg1cAnpjqcWO1PZLsgKvLLc7NpuFj30P+57+JqYzi3rgHotv1yXrP2baRBkDWXH9a9SWC/SaBag0vPw+nvhmq57m23OJvfuxlzs4V+Jff98yOa5aHDRF5QyAuIq/jBWTpblTy0Kb3rJhhpdV96BtId0tf5OV26MkDqmkjxEYhMpaaXTKpRL/hHkxFdeyCBghccwFNb31dCnNUVY1rK62hxSiHoeZri/UdrZpgKnnjT/NqE9gxG9sutLaSV6TTH1w+LBbs4x2fysGdVyGwr8P6dbMxM+7qu9emrTPbLKxO0dhlmrXRJI7Gms46vhV5W6O9AU6dfy8A965d3NPDp/0m7cT2ijdzT5ivS5e4cLTE5XvxE3lNndu8KLMDnXVr+O+T1xaiuZv2vHL3IvzkeXj5eY5OZblTG7fIs5U8t7etd/P9p6qsU2RtaTSBNHFGu8aamNsm8owdvpRNM1/ODC1h81Nfu8uP/vqr/LHzVf7Pv/g+EgnFo7PxEnnaa1LbqR/cirxzRRc/0CysD/c9Xev4rLdcfvDuj8PbnwG3xnsL67Gxa6756QdW8gpBk4X99iZf+wNYfRs+8BHqrs/feP5FEgp+8SPPbvvcPqyIyBsCZ6oF1tv+WHuLglDT7YVkIpGXjSxV61QKDn6gaXR7oz0IreGl52AEF/W9ENgkr21ixi7cp1QHpWBlBGlS0Yy8QZvOTCEG/WeA37Eib2slL1+hFNRw/XBoO+HXVlq0vOC+Im+2kGat7Y93No/XJEhFg1e3XFTSebRKUlLtfuVtWCzaC9TRqSzcenHjjrVr8ai++2aEwtZKXrZkehQa66NJHI01nTW8tHkv77TL/HVnjnJLV2kv7i1h0wmadHYSeYU5sxheeoPH54u8ebe5/13sYRHZNbf2KaYcOokiic7w+1kvLtQ5MZ1jOu+YReF//GsmJffuaxydMnbNsZ5TunXIThm7Znbz+yTnJGmmZvFrQwyOeFjwGrafdWe7JhjL5jAqeS9dX+NHfuVlnjpW5ue+/4/gpMxyt5RNM1twuB4ru2ZuW4pxJPJO5cx15+qQE9yvLTX58dQv8tjSp+C93wvAezN3WRp78EoTkhlavR2ux9Bf25bVEMYovPwcZKYInvjz/K1/9wrXllv87If/SL/aexgQkTcEHrMWg2FHz+8G1zdz+rLahXRhYP7M+kh7zzax9Ab85t+CL310tM/zLgm7kcjbIjBsqEHSq4/MFjc4CD2iUnBodHt0e+OdqdjrNmnrDIXMluGrhSpZfx1FOLTK9MV+6MrUjvfP5B2CUNNwR7wB8SD8Nn7SnNS3XVSUQjslSrSHXslbrLk4yYRJeb31JSifMBewtetUig7L40zX1JqE36bF9p684pRZfLTroxd5rh/wuSvx2DQCoLNGJxXZNbcvQGYKDneSJ3DW397Tw2eCJt3kDiJPKag+DsuXOT9fotsLY1OJuJ9dE8B1Zsj5a3i94VrNLt6u8Z4T9rz+n/8BLF825/X1GxwtZ+mFerx2Z2vXbHS3V/IAwsIc6c5S/7o9KSS69e0hI7lp0yfuGzFzdq7I2/ea+3IeXbnX4K8/9yWOlrP8m7+2vSrz6GyeG6vjd1/R81BB11Q3t7YK2LFGx9LmOK8OOfch99l/wvelPsPqH/nb8Gd+EoDHkwvjH6Hgt8Ep0PaCnUWereRN0drfa9Lz4PXfgPf+RX7iU9f5zKUlfuxDT/ONZyt7f8wYIiJvCJypmmbhcVo223YYuxNGdk1byeusHVwk+40/NF8XXhnt87xLtE24cwpbBIa1a45yIPpys7spWRPMAhBgfQQjG3ZD4LZok9luNyvMoXRAmfbQNiwuLtRJJRTn54s73h+9N8c6RsFr4SdM+Mm2GGtA5cpMqQ6LQ7aALdRcjk5lje//1pfg5Adh5tSGXXOci1S/g0LT2SFdszxjFh/d5tpO/3Ko/PIL1/nwL36BK0MOYtgz7jqdpHEG7NgvArhTZ6h0b6LD3QubXNCimyztfOfc47D0Bhfmzf1vxiVh0wYlbA1eAQhys8xSH6p9stnt8fZyy2wcvfZx01PzLX8XTn0LrN/oj1EYa/hK367pb6vkAWSmj1KhxldvTVZ4UcJr0iC/3a4J/TTwc0eKNLq9PdsGlxpdfuBff4lUQvH8D/5RqluuwxCJvBhskgwk097PrlkO6xQzKa4Ns/L4Bz/FhSsf5ZeCb6fwHf/IrBeL85zWt7k37n5WrwVOkY4X7Hg9JpVBp/PMpfa5TmktQdDlJe8Rfv6zb/ORbzzFh//oqb0/XkwRkTcEHpnJkUoo3h5jwmbHiry0diGd33TirESjAkZcyetdMyKve+NFY90cN90GoVZkcjukawJ06yZ5dESVvOoOlTwY/d/hnQi7LTo6s32RancOT6SbXB1S+Mrri3XOz5fIpHbYkcNU8mDMSZJeCy8SeTvsHKrMFJV0d+gib3HdDkJvLsH6dTjxQZg5DWvXqBQyrLf9kTTbvytsX0SL7Qv3KSvyvObog1c++6ap4r18ffSC8h0JQ+is07IibKeePABn/gJlWty5c3vXT5HXLbzUzhsizD0B7RXO5MxiLxaLVOjPhGuRI79lUZYozDGrmkOd6/e1RfN8H5xah9/8O3DyWfgT/wtMP2oreea8O9a+vG59I3hlp4CeuZNUVY0vXYvRvLYDIOXvMC7A9m4OjlGAvSds/tfX73B7vcP//Vc/eF/b3alKnoV1dyTz+HZF/7OzQ/BKbhpQqM4qp6v54TnFXvq38Lv/mJdK38rPF/57MtFntnqBo/4NWl5Aa9StPQ/Ca4JToOX1dnRLAKjsNCez3f0VVuycyl98uck3na3wD7/rqb0/VowRkTcEUskEj1byY63kdaztIx244OQ3evI6a8wWD2YhHVz/Q3o6QcZdgkYM4qG9lj15brnIZjcqeZURjDXwg5DVtre9kpePhq+PWeR5tpK39aJSMDuHT015Q5mVp7Xm9YXaffvxgL6VeKy9il6LrrpPTx5ApsRs0h2JXfP4dA5u2368k8/C9ClYv0GlYN6zw070fNf45lzWYftmwEy5jKvThJ3RijyvF/Klq2YR/MrNGIi8bg3QNJVZhN6vMX/u1NMAXL301V0/RUG36KXvJ/IuADDVvEo5m4pRT1GDnnLw2B6UkC5VmVX1oYq8127XSNPj2Rf/PqDgez4KybQReV6TY455rrElbPa60HNNT94OwSsA2ZmjFFSXL7+1+42Ah5l0r2V68tJbevJg2xiFvfbl3at3UQred3LnFgGAR2bzBKHm9tqYZ31at9GOPXmJpHlt2iucqRaHMxD94v8Dv/V34dy38787f4vTcwOugbnHmW1fA/R4w1e8Vt+uuWPwCkBumiPpzr6SwIOGGe2iilV+9sMfIJ08nHLocP5fjYHHxpyw2fbMzksqcLfYNdc3KkijXEg37pBp3OQ/h88CoG+/NLrnepckIpG31eseVfLcOrMFZ+jBK6stD63Z3pNXPIC/w7vBa5nF+9bFR8FEnl8o7XOHzHKv0WW56T1Q5MWikue36T6gkke2TDkxXLtmEGru1l1Tybv1IqgkHHufqeQFHscTRkCN7XWJKnk7WPCSCUVTFTaFJYyCV26s0fFNX8YrN2IwrsGK2jpFlILsfarTJ8+ZhM2V67tM2Ax8cnQJnPt8XvoJm29wulrg2pCDGPZMt4mXzJNKqH64RURuZp5Z6iysDU+Qvna7zj/MfZz0nVfgz/+0sTiDEXlAtXeXZEKNr5LnmupM6JRpecGOdk0KRwC4dfN6fAJ0Rk3gkwpdGvo+dk17PjlSylDMpPZcyVtqdpnNO6QesGg/FZeEzfuFFkXkK0bkVfLcWmvvr7f1rd+Dj/8NOPn16O99jjdXPM5UB/p/q4/j9BrMsT7eWXleC+0U8HrhNmdAn+w0s8k2t9c7e+5rra+YQsR3fP3XmQCnQ4qIvCERjVEYV6JXZNdMBh0TvJLKGNtmZ41sOknBSY7WJnjjBQB+qfen8HWSlTe/MLrnepck/CYtnSWzZeGxuScvw3rHH+qFNjpBbhV5/UreuEWeb6Lxt/cAGBvemWybm2udfYclXOzHnO+wo6o1aL0RCjTmnrw2tpK3dUMAIFOmSJvFmju0MSTLzS69UJsZebe+BEffYyrwdsE6H5pdxpH30d4Pzyx+OmS2Ba8AtBNFEtZqNCo+d2WZhILve/ZRLt1t0HDH28saLUJrFMmnk/edoZSePYVPCv/em7t7fLvgC5z79OSVT4BThKVL8ekpAug2cBP5HRepqeIcjgpYWRleeE72+u/xEf0J+OAPwtPfvXGHFXnJ+g3mipnxVfLs56KbekDFt2hEXs5b4Y07o/0cxQb7/m6r/ObNgEjktU3VXinF2bkCb+2xSrPc6O7YhzfIqYoRN9fH/RmKevK2jpWIiETeXIFQ70OUNu7Av/+w6ev9K/+BFS9Fw+1xujIo8syQ+HOJhfHOyvNa9FL3CUKLyM1Q0jaQZo8b0s1VI/Jmjhzb079/WBCRNyTOVIt0eyGLY7qwRHbNZK9jKnlgLJu2mXm26LA6ygXjjRfwVIY3M09yWZ/EvfbiO/+bEZPstWir3PbFWCIJTqnfk6f1cC2UUTrV1gvNdN4xIxvGLPISvTbtnSp5ttH7uNMiCDU397n7fvG2Wbw8eWzLotWtwb/5Tvj43yDvJHFSifEJX637lU0nmdh59zdbJheaXdRhVdai+T7HS2m4/bKxagJMnwag6pt49fFV8jbCNLZVwgE3WSTljzb443NvrfDek9P88cfn0Jrxh1RYkbcaFnYchN4nmaKWPUGheW1XGyU9O1xeZyAybOAAACAASURBVO5TyVMKqhdg+RKnKnlur3XG17M5SLdBJ1HYbjeD/sZRc/3uUJ7KXb3N32v+C5ZyZ+FP//jmO63IY/0G83aMwlhwzfu0nXiAyLOuiaqq8eK1GFiRD4K++N2SHrulkgdwbCq357/fUnN7svVWjpQyOKkEN8ct8uxr0tyh9xmwIm+1L8b27LBZesOkVv7pH4fcdN/6eWZu4G8x9zgAZ9UC98Y5RsFrESTNGvZBds1cYK4/e7Vsdtfv0dEO89Xqnv79w4KIvCERlb2HFVixW9peQIoeSvdMBQ/s/BmzcKgUMqMVFzdf4I3ked53ao5rzgWm118be/hKqtfGtb1W28iW+z15MNzFdFTJO7LlQpNMKKZz6bFX8hK9jqnQbD2BphzITjGXNAv8/b6XLy7UOV3JU8qmN250a/BLf8Eksd78IkopZvNjTJLsdUEHtHWGbPo+p8NMCafXBPTQLJvR45zWt8FrmNAVgOlHAEXZvQWMUeT5ZvHTS+VI7lCx8tNlMr3RibyG6/Plm+t8y7kK7z9prOev3BjzYthumL2jyAN6M2c5xSKXd5GA6UZppdn79xIx9wQsXeJUpUAvHP6A5D3hNemoB9jNAHf93v6fJwzxf+2HyOPyxrf8y43NzIjcNGSm+uErY7Nr2oV7W5nr8I52TVvJO5tv88VJCV+xNlZ/a7BQpmTs6gMib66U2XNf2HKzS7X4YPtdNBT9+rgtz92NSt6O1sT8LLSW++vLPfflRbOLi/PAhlg8M1jJKx1DOyUuJG6Pd4yC17r/SKOI7DRpz2ym7DU/IGzeY4UyR6fvs0Y8JIjIGxJn+7PyxpOw2fED8tgPphOJvOkBkeeMzq7ptdCLX+Xz3nnOVIsEx95PMWzgLV8bzfO9S9JBGzdxnw9wptwfoQDDTbyMRN5OlpGxR+NjxG9bZ++78z4dmvfMfvvyLi7WeGqwH8+twy9/Dyx+GR75BqjfhqBnhsSPy65pxUxTZ3Z+PQAyZRK6RxZvaCIvquQda75qbogqeakMlI+TaxmRN7YkVtuTR3qHmW1A4JTJhaM7133x6ipBqPnms1Wm8mnOzhXG35dnF6ErvfsImkEqZzmj7rDSfPfvF69l/v9U9v49rMw9Do1FHiuaHuxY9OV167R2mJEH9MOcgubS/q3Of/gzlBY+xz/u/QCnn/zgzr9jEzaPTeXGZ9e0lbwGVuQ9oJL33imPF6+tDs0GHmusXbOX2uLsUGrbQPQjpQy1jr/rmbJaa5Yb3jtW8sD05Y09vGigJ+9Bds3pXJqZfHrvA9GtFTbadLm63CKVUJycGVgfKYWqnufJ1J2xV/K6iSgI7T7X5Nw0ym9xspzacyUv0V5ijSnKg5vQh5ChiDyl1L9WSt1TSr02cNusUup3lFJv2q8z9nallPpppdQVpdRXlVIfGMYxjJu5UoaCk9xX2s9+6HgBWeyCMNrhHDhxjiJFss+tF1E64IXeeR6bK1B9/BsBuP7q74/m+d4lTtCim9g5QpnslLVr2vESQ7SyLjW6lDKpHU/asRB5QYduIrtjhYZCFae7ykw+va/I5lrH5+ZqZ6Mfz63DL/8FM0Pxe5+HZz4MOoD6LSrjfE36PRE7VDYj7KK7RGdoCZuLNZdcOknu7ivGVl05u3HnzGkS69eZzqfH2JNnXpfQ2VnkkZ2ioFsjiyD/3JUVMqkEHzhlrFwfeHSGV26uj3cxbDfM7vVy71jJU5VzZJRPb+3Gu354v2XO1Yncgyp5xlJ1BpPKOPaeIoBuY+c5X9C3a5bC2v6dJBd/nev59/Db6T+5eXE6iBV58+UsDbc3nih4W7Gqa3PtKe1UyUumITfD2UKbu/UuN1fHnPJ4ENgKZ7BTemx+dlslD9h1AEjLC+j4wTv25IFJ2Ly52h7vOSWyxe80QgGMKAt96DY4XS3s3V3TXjFfrTX22kqLR2fz29sT5h7njLo9vp68MAS/hZd4QNo19NPj31vRe16nOO4KrfTMnv7tw8SwKnn/FviOLbf9A+BTWuvzwKfszwDfCZy3//0w8HNDOoaxopTizNz4EjbbXkBO2RNiZNcc7MkrZFhpdUdzQrv5BTSKl8PzPFYt8PT7v4GuTrF+ZbzhK07YwbuvyNtcyRumyFh+QE9AHEReOujQS95nkVSYg9aKDRLae6Xm9QVzQX/qeHmzwPtLz8ETf3ajf2btuq3kjSlUwwaMNEPn/v5/2yM1k+wMzR63WOtwbDqLuv2SGYKuBgT39ClYG/NAdPu6JO4j8lR+mjIt1kZk6/n8W8s8e3qWrO0HfObRGVZb3nh33jtrkMpR802Q1YNIVa1o34XI67VNBSgZJSPvhBV5M623yaQS3IhFJa9JQ+e2j6oBKBiRV6Her17vmbWrvB4+ytPHp1Fq59CbjVl55rw+lmqeFTO1MKrk3adSUDjC8ZSp5IzKslnr+HtOHxw6tmoVOjuIvK2VvPLeRN7yfULPduJUJU/LC8bbI99t4CVyhCR27H2OKm9mjMI+EnXbK2Y9mDSf0avLbU5Xdzi3Vy9QDVdo1cdkjbfOGvdBI42gL1Yfnwp4e6m5p3VtvrdGN1PZ23E+RAxF5GmtPwtsPUt9CHjOfv8c8N0Dtz+vDS8A00qpQxFvc6ZaHJvI22TXTA/aNW0lr+DgB5rGKHY2b7zAevEsdQo8Nldkqljgevoxsku7nxM1TDJhGz/1gEqeW2cmnzZhKEO2a95vJ3G24Iw3SbLnkSSgl7qPyMtXoLW07/fyJZsY9/SsMhbNhVfgL/1bePK7zC9M29jz9RvM5tNjHxVQDx5QybMi71Sxx50hVfIW1l0eK2m49/qGVTNi5hQ0FjmWV2O3ayYyO4u8dH4GRwWs1oafDLjU6PLGnQbfdG7jAvzMo7Yvb5zz8jrrkJuh5fV2TBwdJDdjLmm6+e570QJbKUzlH1DJmz4FyQwJG74ydrsZQLdBPczunEybzhMmM8yoxv5mknXWobPGV1szvOfEA+ysdlbeiawRd3fH0Zdn7Zq10AjNHXvyAIpHKPprlLIpvjyC97XWmr/ws5/jn/zW60N/7D1hxW+4U7DQFpE3VzQjbXbbl3e/0LOdeNSOURjrZ6jbwEvmyaQSOztr+iJvlZPTJoxmT2FL7eX+hovWmmvLrc3JmhF2E6nYeHv3zzEM7HWnox4w0gj6I8LOlnrU3d7u1w9aMxXWCHKHO3QFRtuTN6+1jiZi3wHm7fcngJsDv3fL3vbQc6Za4NZae9c+8mHQ8XpMp6yAGxR5fht63kbAyLAXjWEAN7/IW9n3kneSzNsduFbl6zjtvUm9Mya7WRiQ1V16W5O8ImxPXiqZYDo3XJHxoHSv2YLDWssbn0XEDrkOk/cRv4UqtFd4rJrjbr27Z7vTYs1lJtll7jc+DAsvW4H35zZ+YeokqASsm0pereOPzPr3QOzrUQucftVoG9au+Wg+YGFowSsdPpi+CmhTyRtk5jSgOeesjjF4pUUXh2xm5wCDTMnspNbXlob+1J9/y4QEfPPZjQvwhfkShXHPy3OtyOv2KN6vV8SS+//Ze/PgSPL7uvOTWfddhbNxN9CN7p6D0+y5ecxBi5REHZS1OmhJlrSx8so6YtfhIza8u/YqHF5Ltna9u7LDXq+OkGVZ8hG21pJsUlqSImeGM5yD5Fycoy+gG0B346gC6q7KrMrM/eP3y0IBqCOzqlAgRbx/QGKygOpEVubv/d77vpcUjzul7Lw6wKyIRXAg2kHJUz0yYfMa8yORkyd5Rh3qFXJmkHCgxedHUSA8xqhS6K8Qfe8WACvGOA/OdCLBwiEwbYnr8kSUvGoe/DHymrjHt5zJA4iMo5S2OT8R5eb24DeHN/Yq3Nwp8cb6N0HHJDRsrEqgRUXICSl5wMkmbGoFKm3qR4AGMaOcYTIRxLToLRSlnGkQxq28RqVmHEzWtDF2AYDx6u0TfR5XEH+/tjN50q45KzdzNlxuINXKWXzUUWUA0p9nDCV4xRIrWlerWkVRflZRlK8qivLVnZ3BLySOA0tjssvkBB68lZpBwictb80zeQDV7H7AyKBnfLbfBb3A16wLLI5FGjaa2LnHiSkV3n7zhErRpde9LcmTM3l2V9ug0zXbPWRSYT910yJfPYFZEWjY8ExfG5IXHgPL4EJCbFT0quZl9nb5V4F/JOyIP/zbBwkeiJmU+EzDlgiQPQnLpjwf2bqvq5I3HaoNJLGvZphsFzQesGSP2swjBw+QKudZb/pEFc6q0iacBwjFRgAo5gbXf2bjxRtp4kHvgcW8R1W4PJc8WZJX2YNQkrJmtCY0TVDCoxgoeCvOz49VzYuU10Cw84HjF2HnfRZGRVfeyc4UCQteroMSrkTHGFcHQ/LWrQkemO6i5AHjhqggGVRQkitoeQgmKMoNsrYkLzoBpR2WjskB9PKKmMO6sV385ihc1wrU8OILtHj2HCJ5oxFRN3ScSt5s6ptAydOLVJVwezITFvdZyhnOxMV9oadnUHn3QOgKHErWtJFaxFC8nFPvkj6JhE2p5JUR/9b2dk1B8iZ94ly4rXva3RLBZoHEKcnrB1u2DVN+tX0rd4C5puNm5fcOwLKsX7cs61HLsh4dHx8/xrc5OCw1EjaHb9ks6wYJj1wk+5tm8gAqe/sBI4NW8mQJ+hdKiyyN73vt5x/8MABb731lsL/PKeTNwmxr14yDWYdamdFIYGA3tGrNoFCttyV5x1HZ4ArS8261I3ky9W0pJBZkvS4+7tv8z3zAfB9+6Lfg/k+1Pii5ANm1/ZL4k7Cxys2AnOHrkK4pdp7PBHQ2c1XMPhdMW/kqlgWL1XdhdHl/M8ZG6iwAc2yzV9b7/n09QS+JLsU2D9loQiwYyvnBzhJZlsWLNzJ86NzoEfvSlfkk793LU9FPaMaosocVSlLS60S6KHmoKlkS+KsZxz9e0fIU2gWYNGP8ImTXOJcQm3tu1Y6BQs5Z7Rrt02mV8BiT3mJ/M3l7qwBse6dYHGsx02VDkrxA8Q7xoPdkuvKqOQjGKVbrhP2e1jY8EPdaLc/yiIfNfHXgITGvrIrPplY3T74PDvZTWFuR3lBK3Ivr4hng9aiMRvw9KXmqQmPjsBOCPg9n4sHeC8YHAa1IWWmTrAkHZvImJcnr6ZpuUvLsZ/rZsRZrAI+XSuws55U7J3Nfkeu2otmF5Mm17YhH/O3cKnl7O4JyhFNnenmX31I4TpL3R8BPy//908AfNn3/p2TK5pNArsnW+S0Ne5D1JObyKrpB3GsreU09eQCVbINcDHzIeO1lrNgUr+VijS4XAP/kfWhKEO69Ptjf5xSanQ7YZkEQ3E99HKSSZ5PF8TY7iTahOekZNNqSPPEgmPWLm2ev13K8clvUV9z/A+0PSi1A9vaxhN84hiS9e7UOwSvSrjnuq6IbZt8zlWIn1mIi9/bReTwQXUaeAJPmJqYF2cpJKJwlylagdZgGEI6L60QrDpbkre2WuZOt8JHzR2clrsylqJsWb985oVL0ShYjkMS06JquCZBXkwR15+dH1XIUrHD769CGnJu56BMF47dOeKYIYK8ebB0cARAZY2QAds28mmD2zGR70gQHu/ISwZPpyqvmIBCnqNXbq3jQ6Mq7FO1vQ60dXl7JMJ0Qi2U3fY3HBq1AwQq1nt1sch3ZGIsG2HGZ8rhT1BiJBDpfI02YHw2ztnuC4UVagVKnjZ1AHFSvUPISPSp5liV68iTJu5Up4feqTCdaz+XXR86fXCF6U9o1dK5QAAjWCyTDPjZcKnnFjFD6E+N/LibFOmJQFQr/BvgKcFFRlA1FUX4G+IfAJxRFuQ58XP5/gM8AK8AN4DeAXxjEe/hmQDzoYywaOJFC9ErNIOY5TPL2lbxjW0ivv0Jx4hEsS2l0BQLg8bIXv4/56tWBxc67grQRWe1Inj38LQvRB3VeGh15sdY7ibaietJKXrtADTvyPKDvMp0IsrLjPmHTNC1G9E3ygemDqZGHkZyHwj1G/ML7fyIl8ZL07tZ8rRcf0LhWRrzib3uvz4TNu7kqs0oav5aB2UeOHqCqkFpgtCYeRLsnUaOglyhZwbZKnirvLbXiAOyTt19q1BN8+Yacx2tF8uzwlZMqRa/sofvEtRDpYtcEKHiTRGrOSZ5HL1Ag3LoUuRlTlwE4WxLBVida6KztR8C3XaiGR0mYuf6CV3ZXWWeSs6NtNqea0VSjcCJKnpaHYJyCVm8fugKNYurFkLgnD9IBtLFXZmOvwo8/IZTN69sn09/bDKuaJ2+FWocWNTakm+fygq7VpB2HHXk2TrwrTxf1I203SBSl0ZU3Evbj8yhsuiVfWkHUMDQpeWdHw6htiLBn4hILyhbp3AlcM3YQmuXH3y6MBsS4hz8KlSyzqZBrJa+SFc/W0clTkucIlmX9mGVZU5Zl+SzLmrUs67csy8pYlvUdlmUtW5b1ccuyduWxlmVZv2hZ1jnLsj5gWdZXB/EevlmwNHYyNQpl3SCm2j15h+ya1SxBn4j9HqhdM7cBuXU2og8BsHTIRuObe5gHlFu8dG1rcL/TKeTig7ZKnjw3Wp5RWcY9iLkF+6Fkp4MdRioi4rRPhNBAYwZNadd/Ju2alHaYHw2z3sPCbLesM8M21ehc5wPl7Nm4DEk4kdRR+VDJ1HztFRTVA/4oSVUsGO/2uWlxL1vhiiLn8VopeQDJBeIVYSlJn0DCpqWXKVr+9imSUgm3EyF7Rvo6/PYn4V9+H5R3eemGmD1ZahHvPRoNsDAaPpm5vFoV6hV0r/h3t91hbkLZN0LUcP5ePbWCrCLoQiBTZ2F0mdTd5/CoygnbzcRmWtHqoEaERwmYFcrlEmW9N0uitXeLm7WxxhxVRzQK0YMnF7wSTFCs1ol1UvLkvXbKW0BR6GlDrR1eWRGbC3/h0iTTiSDXvwmUPLOab9+n2ILkjUcD7kleUWMs2t2qaWN+JMx2QTs5C7hWoGh22CCBBslTVYWJWA8bF3ZHXhPJW2xVnyARmroPr2Ki71x393sGAfk8LnRKu7YhK8Jmk2HXJM8oiOmx2Kld8xRusTgWOZGZvGrNINogeYeCVxqF6IHBBq/IebxveO8Hjnq8U8tPElJ0br47/PAVU5I8JdCO5O0reSMRv7DFDYBk2IPfbWfyGuXrJ1v+7Qm2U/L2ZwBmkuGe5mg2sxVmlR3MRBeSlxIkL14VZOaklDxL9aJbrcvrGwjEiCAW0/1awO7lqjzuWwFvCCYeaH1QaoFQSQyHn4Tqa+lFSlaHxUfD7tyndfLqZ8TX9FWs3/1B3rpxmw+fH23bg3ZlLsnX1/aGHzYibWQVr7hvRB0oeVX/CAnTOcnz1YoUCeP3OngsL38C9daXWUqoJxwBLxITC4TbWnvthMARCr3N5Rk1yG1wy5poX4LeDLsrLyZIQk+R8/1A2jVLXZU8Ydf0V9PMJEOs9OIAeu+P4Q/+6pFvv7KaIRHycelMjOXJGNe2vlmUvDZJki2VvAA7RXfdvukOoWetMG8nbLq0+w0MWpGc1T7gCpAkT5D2nizINsmLjGGYFmuZNh15Et7JS+Jr5uRIXs7wt3fW2JAVYULJcxdApZR2yCmxRm/gn2eckrwBY3E8Qrqoka8Od46mrBtEDpM8eyFWsQvRB1yuvP4K+CK8VplmIhYgFjxY+qrOPAyAtvba0BdltUqHuGY4YNcciQ7OQpkuiJ8x2mY3MeT3EPSpJxMyApjyJuoJtiG/Xr+YaSmlmUmKXUO3UcrpnXtElSq+0cXOB8qQBH9+nWjAy27pBGbPamUsn3jgdXyoBOIEjSJ+j9q3knc3W+ER702YvtL+IZM6i0fPEad4IhsCllakTHu7Jl4/mhJE1fvsybv6JzD5Afj072FtvcM/Nf4+zy60X6RdmU+xXdAGVmXhGHLxWfaI+4kTJa8WHCVCBWrOrhd/vUjZ0yFUpBnLnwBD47si107Wrik3jTpuCEgLuJjL6+HvlltHsQzWrQnnSp5eZC6s9R453yssq2HX7DqTZ7smijssjfeYsPneH8Nb/xbyB2MNXlnd5fHFEVRV4cJklJs7J5+waUklr6U1sY2SVzMsx6nLlmWJ+iIHyZo2TrQrr66DoZE3gp03GMMjDaJ2phcLcpOSdzdbQTfM1smaNsaWxeH5m+5+zyAg1yfZuq9rFynBZMOuWa2Zrp6TvmqGoifV/cA/BzgleQOGbTMa9lxeRTcIowurpr0LrnoE0WsqRB+oXXPtKzD7KDczWmv5f2QJ3RtjUbs29JmAekXYU9qSGZsAa3nGIoMLpdkpVkmFffg87T9ao5HAiZVc6xXxd/AH25BfEDvvpR1mUiFMy71yVd4SD4fI5FLnA2NToPpEwmbEd0KzZ8VGnUTHhXswjqLlmUwE+lbydrIFlo2Vo/14zZBW1jllZ/Ddlg5g1cqUrfaJiQCaN4qvH5JX3oX1l+HCd8GF7+RzD/wqDyi3+O43/7uGDfAwHp4XD+ahz+XJjbKSKj43Tmby6rJo12khesAoUlE7LL6asfAR8IX5qPV1bn8z2DU7hUdId8CIku9tLm9XJGveNiedK3nAgirmO4cavlKriNTmYIJCtU404Gt/rDcgnkOlbZbGIqzsFN1vhmZl5fDGq41v3ctVuJ0p88SiiN9fnoih1c2TtfUCil6Qtt4OM3nl/RlWuyvPaY1CQauj101H9Qk2FiTZOZFzIzdI9hzZNcW1PBkXFmRX10mD5I3s1yd0UPLwR0h7JkiWbzn/HYOCXgIUcjVvd7tmSNg15yRRd2PZDNV2qfpH+nij3zo4JXkDhl2jMOy5vErNIKTo+yqeDelbBqEuDcyuqRVg6x2Yf5KVneKB+oQGVBVz6jIPqSt8+frg+7Q6oV4Viw9vqA2ZabZrDrDWoFNHno1UxHdiSl5dkjxfqINiEBmDcprppLiW3Kbi1TK3AYidOdf5QNUDyTmRsBn2s3tCPXl2zUZnu2YctAJTiVDfwSvx3Lv4qHUmebJG4WIgcyLkV9FLlDuFaQA1X5ywWUSr9zjPcuPzYJlw8ZMA/H7uQf5B6G/hv/d1+L0f3U+CbcKlqRgBrzr8uTy5UVZQxP3dSbqmFRZKjZZzQPLqOn5LQ3eq5HkDsPQs95deIVvWyZ3EZwcOkbzOds0xtUe7pt2RxwRTyS4dgtAgeWcscd6HGr4i7at2umask10TIDIBxW2WxiOUdMN1Lxw5m+S91viWPY/35JIg18uT4po60YRNy0K1g4Va3VMCcVDUI0oeOC9E33FRhG4jFfYRC3hZOwk1XF4re/UOyc4gSF5lD0yDM4kAZd2g4KZuo0nJu5VxQPKAdPAsZ/Tbzn/HoKCXwB+hVLPah9HYCNlKnk3ynBH1mmESN7KNTbg/7zgleQPG3EgYVRl+V15ZrxNCA9+hD29TyehIJMBuSR+MdXLjNbBMCuOPsFeuHUzWbEJw/hHuU9d55frd/n+nCxhSyfO2U/J8YRFNLCsUoA8l77XfhN/5FBh1RyRvJBI4sZm8WlWQvEAnkhceg1KGGUny3C7M1Jx4OHhGznY/ODkPe7dJRfwnNpNX94h/Z8eHSiAG1TwzyVBfBdRa3WCx+r74P+1CV6Axr3jBnxn+tWJZqPUyZQIdLTNmIE6cUu+bI1c/Kxa50w+j101eXd3Fuu9T8EO/IRS+3/90IyjIhs+j8tBs4gSUPPH7cpb43HTtyQOUqCB5VZnk1hFywad7Oyjsh3H+48Srdzmn3OX2ScXAawUMTwgTtauSdzZY6a1GYW+VmuKH6BQBb3cF1SZ5djrtUJU8OaNqOalQgAOF6IC7uTyjBnlZMbzeRPJWM8SCXu6bEhuZy5PimjrR8JW6hmrWKLYLFlJVab87mK4JsO2wRiFdcF6EbkNRFOZGwiej5MncgKwR7JyoGx4Vm2HV3H5XnptrupQWjplAnNV0iYjf03WNUogtMm/ewTKHHEijF8EfEc40h8ErM1LdX991dm/ZylcZVfKN+/Ofd5ySvAEj4PUwmwoPVckzTYtqzSSIdlTJk7sdIOyaNcNytwvUDmsvg6JyI3Af0GFnaPphfNTJrr7uerarH5jVAkUrSMjfxi6jKGL3sJprdNdlep3dWH0BVp+DN3/f0UzASNh3Yumada1ExfIT7mQjknbN6R5JXrC4QUGJ7aulnSAL0UfCA54XdYpauUHyOj5UgnHQ8jy8kGIzX+35872Zq3JFvUE5OAnx6Q6/LwHBJGc96eGfl3oVxTIpd5qzAggkiCvl3qzHRg1ufAEufCeoKq+v7VGpGXz4/Bg8+EPwF/8F3Poy/NsfF8mWTbgyn+Ibd/K9K4i9QLohsojFeNcFCOCJiWANR0qeJAc1n0MlD8RcHvCs+sbJha9oBWpece9v3zOZBMXDbKDcI8m7xbZnkpkRh1ZW2ZUXLt/B71G5N0wlr7pP1g3T6hy8AmIur7jNotwkXUm7GGvI3xWL/9AI3H29UST+8souTyyONOLnowEvM8nQydYoSMU3307JAzF71qzkxVwqeV1Cz9phYTR8MpZneU469uTBgTC0M5Lk3XND8uwidEUR9QljkbbBVjb05DJhRSO/ecv57xkEpJJX1uvd555DSaiViXoMUi668rZ28ySVEr745ADe8Dc/TkneMWBxLMKqm5t1n6jKxU7A0sB/aDA9lDpg1wQGMw+29jJMPsCNnLhZtLRrggiXAJaNG7yxPjyLlaUVKBEk2EmdCSZAy+PzqMwkQ9zsdY5SztxYX/wVioWCIyXvpHryTK1EmUBnu1lkDMoZgh6F0Yjf9cIsrt1jz+8wmji1AOU0E8H6yVhY9SI1j0O7ZjXPM8ti9++5azs9/bq72SpXlOuUx690Pzh1llm2h3+tSPWsROcYazWcJE6ZdC+bI7dfAi0HF4RV88UbaVRl32LG5U/DD/wzWPki/JtPi40UQ1gSH55Pohsm797tM/TFDSp7gMKeIRZZTuyagYRYRBh550pe3edCyUvOY45d4mPqGyc3b6UV7nQCbgAAIABJREFU0L1diK+qQniEKV+px5m8W9w2HSZr2kjOo+TWmYgH3Kke/UITZL2kOrT1RiegtM1UPEjQp7pT8myr5v0/AIYGm2+zJTegnlgcPXDo8mT0ZBM25fXdsWqjyXUEEPF7CPk8ji2s+0qe8woFEAmbG7uV4QfTNIUWdQ1eAShnmJIF5q6qQcq7+0XokuR1xfgFAPJ33nX+ewYBSfIqutG9SsauwZKWTaczebs7IqQo/G1QnwCnJO9YsDgWYXWnNLREybJuk7zqfkeejSYLxH4hep8zPkYdNr4Kc0+ymi7hVZX2D+DkPGZolMtDnsuzZJlzxxtFMN7YQX9oNsFbGz2S0OIWpM6iFO7yafMzXe0iIxEfRa0+XCVCwtRKVAh0eaiMgWU0rBBuEvEsy2K8vkkpPOvsBTJgZMGTpqwbVGvDtoeU0VWxcO8cvJKAeoX5pI+lsQhfutobydvd3mBe3YG5DlZNG6kFJo3N4ffkycVHhUBHW6IvkiSu9GjXvPYn4PHD0rMAvHgzwwdmkyRCTQrzlZ+AT/1TuP0V+J3vg//tHPyH/4YPlf6MOMXhzuVVshBKUq6ZeFSFgIOag0g0RtEKYhYdXCtSATL8DtTvJqgXPsETnqtsbvd2PfYNvYimyuCijpazMcbUApv5qrvFtGVh7d3iem2sEbDgCHaNQnzIXXny71hCvNeOPXkg7MrVHKqpszgWddeVl10TXx/8IfF141VeXhHzV08sHQyVuDAZO9mETZvkEWpftXGI5CmKImoUnJK8oo5HVRrOHKeYHwmjG+ZwZzehqX6km5InZ8fKmUYYjauNi3IGIqPUDJP1vUrnZE2J4JRwaNU233f+ewaBWgl8Yj61bbKzDTusp5pt1Cg4QT4jRodio1P9vNNvGZySvGPAuV6HqHuEXeTpN6st7JopsUCxrP2Otn4XjVtviw/j/JOs7JSYHw23T5NUFNSZKzzmv82LN4ZH8hS9SIlg5zmrYKLxUH5oNsntTLk3G2VxGy58kvLZj/ML3j9iOtB5R2lE/h32TqIyQC9Rtjov3vcL0dNMJ0LccdEhlC3pTLNDLeaO5E3LQvShq3l6iariwK5pV3FoBZ65OM7LK5neCOmG6IyMnftQ92NTZ0nVNsmWq5jDXJzJwJOOsfhAIDpCnDIZt/c5yxLzeItPQyBKoVrjjfUsHzk3evTYh38K/ocV+PS/hkvfD6vPk/jsL/D14M/x1Is/DS/+E2FbO25U9iCUoqSJWZFudieAWNBHxoqjlB3c9+SCz2pX+dIOy9+Jjzqxza+4e92goBWoqg6U8MgYSSuPYVruFtPlDIpe6EnJEyQvwFZ+iMFFctOwqIhz0n0mz77X7rA0FnFnA7eTNeceh/gMbLzGK6u7xAJe7p86uFmwPBFFr5snV7chn7MFK9y+quYQyQMRvuJ0Jm+noDEa8aOq3T+bzViQNuChW541W8lrUytho8muGfR5SIV9LpW8NIRHWd8tY5hW19AVgJHxafasKEr6qvPfMwgcUPIc2DWhUaOwsVdxJKxU9oSzIpw6JXmn6BGLvQxR94GKXGz6zMpRJS+UBLMGemnfrtmv/WvtFfF1/klW0sXG0HhbTD/MvLHGe+tbFIbUHyhIXqizXTOwr+RdnhWVCm/dcVnurJdBL0B0gpsP/S2iVLhy+7c7vmQkItSKk5pBK3ex4RGxHyppZlIh7madRzbvbK4RVGqoMh2yK2TAyLghbrxDr5aoldAUQbq7XisA1RzPXBhHq5u8srrb/vg2iKS/Th2VwJwDu2ZyAa9VY8zcHW7vZk0sdroFr/gjKbyKSa7gUlFLX4e9Vbjw3QC8urqLYVp89HybtLNAFO77fviL/wz+5jX4mc/zudSPi8/u5/4u/Pufcvf7e0FlD4JJUXDtwKoJEAt6yRDHU3au5DmaY23G3JNU1TDLuZfcvW5Q0ApU1DA+j9K5xD08SsQQ14mrGV+ZrHnbaUeeDdmVtxjR2cy5jJzvB5Ks50xJ8pykawIUt1gaj7C+V0GvO5xdz65B9IxIWp19DNZf45WVDI+eTeE9tOl6QYavnJhlsymFte1mQAuS507Jc1eEbsPuylsbdniRdEx0PCdwgOSBqFFwu1HSnKzpxK45Hg9yw5omkB1yV55ewvRF0A3TQfBKs5IXRqubjlwvtbyckY6cpmueokcsDrlGwbZrek2tBcnb/yDs2zX7XEivvwzxWYzYDLcy5UZtRFtMX0HF5JK1yqs9LIx7gadWksEr3WfyAB60SZ7bucGSvGFEJ1n3neU/Gk8xfe1393dZW8BW8k6C5Km1EhUr2HlWxLaHlESNQqVmsOcwoj2/eQOA4ESXjjwbkXHwhkjpwid/EkpexYmSZy++tTxPLo0S8Ko814NlcyL3DVY9i0dnZ1shtd+VN9SETbn4KFudlXBF7qRW8y4/09c+K75KkvfijQwBr8rDCw7KaVUV5h7jzsN/k09UfpnKgz/eIALHimoWQinKTlLfJGJBLxkrgU/LOPj5cnMpkHD3vrx+NlJP8ITxNar6AAK13EIrUFG6KBEAkTGCuljAu5rxlR15a5bDjjwbMmFzyZehUjPIV4Z0bqp5UDzkDPGsdZSuCbIQPYJhWs7JRm6t8e9k7nHIrZHf2difa23C+QmxEesmYdM0LUqDCGmDAySvrdU5lBLPY2P/WSOUPOfBK41RCaMmNmC1gvhslXdFymRhSxTH1/fvp9PJIF5VGf5cayN4Jdh5VMAfBm9ovxA94cKCbNSFkys8ympa/PucKHnRgJdbyiyJ0qqz3zMo6CXqXru31kGFAkBlr3FvcGTZLMnnduQ0XfMUPWIqHiTgVYcWvmLbNT1GpXVPHkBlj6DPQzTg7U8tsSwRujL/JHezYtdxqdtNQ4avPOxb5ctDsmyqddHz1d2uKRZX8aCPpfEIb264VPKK+yRvp6Dxf9Z/GAXgS7/S9iUNJe8EgkbUerlroMa+XXPHdY2CtiMeCokphyRPUSA5T6wiosCHSnyNOhg6FcTCoHOFgk3yCgR9Hp5YGuVL15yVXDdgGpytvs96+AFnx6cWAZhXhhy+IoNXDG+okdDXEkFBSLSiS5J39U9g8kHRkQi8dDPNo2dTnZXUQ7giS9HvmKPioV0/5vNj2zX1uqPQFRA1C2niBLTu58eS9yG1Xa9nBxTmPsa0ssvWja+7fm3f0AoyHbDLOQmPolazeBTT3eanJPAbjDdCJxxBkp85RRaiD2veqpqDQIyiJp7J3Xvy7HvttnsHUHa98Rli9nEAHlav80QLkheRCZvXXCRs/taXV3n6V784GKu4Zs+cxtpbnRsb0vvP4Il4kEK17sgan7bri27+Gfyvk/DLU/Ars/AP5+FXF8VM7z++AP/HJfjdH2y8zutRmUmFTsCuWaDuCXeuH7ERHm0UxZ+JB9nMObQgV7OABeExVtNFEiEfqXCHZO0m7ATmidT3DhTUHzv0InUnQWhwJHgFnBWieytpUcni1hr/LYpTkncMUFWFxbHIEO2aYrfNU6+A/3BP3v4HAUT4Sl+F6Nk1KNyTVk1nxZrEpyA2xbPRjaGFr3jrZYqWSCxri0BcqBayC+bybNJ9+EpxS3yNTrBT0NhSx+Hxn4U3fl+UxbdAQ8nrtbKhD3jqFaoEOwdHNNlDbJLnNLnK2hMdeanp887fVGqBYGkDYLjVEjU5e4a4TjrOctgPBGmre+bCOCs7Jdbd7P7uXCVMhd3UB5wdn5jFQmFO3R6ujVXO5FmHOzcPQ5K8eslFZ115VzgBpIq3Xajy/maBD59zZ515YDqOz6NwrSL/LoV7rl7vGpU9CAm7ppOOPBDPgYInRbiWBbOzBc+o5ihZAUIBB2Xfh+C/9F0AaO//f65f2ze0AkWrQyy+jfAYChYPJOvuovz3Vsl5R0nGE53toIchSd6kKe7PQyN5Wh6CoiMP3Ch52w1HjKOOXdOE3AYkJMmbeoi64uMJ300enG5t+b0wGXWl5H3mG/fIlHSKg1CIbZLXqSIkJMNieihEtyyLdFEXSt76q6Ja4jt+CT7x9+G7fhm++x/B9/zv8L3/GO77FKy91FgTgbBsurqXDwJaoaFadSU04ZEDds1MSXNWSVVKN15/K112VJ9gIxeRG7U7Q5zL00voTiqNoPH8sYNXoPs6pWaYhGp7VHwpscH8bYBTkndMWByLDK0QvaKbgIVab6Hk2btjTQmbfakC603zeDIJrG19QjOmr3A/K1zfLjr22PcDX70kZvI6lefaNwn5ALo8m2C7oLkrz22QvMnG4Lfy1N8QBPLzf6/lSxIhH4oCuw4tkIOE16ige4Kdb/Rev7CNldKNolGnSp4vv06GZPsS+lZIzuPJrw//nNgBI6a/uxJx6Fp59qL7KgV9XSgt+oSDeTwAbwAzNs2csjNkJU98rq3DG0aHIc+JWXGhft/4vFiAXRTVCbbl9ZkL7qwzQZ+HB6YTvJmV97vjJHmmKCK2g1ciAeeKY8mbQsU4Mmt0GEY5R4Fwd9tjC0zPLfGuuUBs7c9cv7Yv1DUwaxS62eKhMef7UKrOTVck7xZ3lTPurJrQ6MpL6mLWd2g1CtU8BBP7JK+bkucLiXttcYt40MdYNOAsYbO4KWbtbbumN8A1dYmPBFePzOPZuDAZY2WnRN0BOciVa7wpRxcK1QGQvGoeXfHj77SJcWitAjAu0yS7WTbzlTq6YQolb3cFErPw1N+Aj/z38KFfhCd/Dh7/b+GxvwJP/FVxD7q9P8c6P3ICXXl6sXv9iI3w6AG7pmV1PydA4zXCrllicdT5XGslITdq09ccv6YvGDUwdDTFQdo1gMcr1lmVLJGAl5GIn/Uuds2tfJVRctRC3x7zeHBK8o4Nl87EuZUpkR2CJa+s1wlQQ8FqXaEAja68sai/P1Vg7SvigzVxPys7JWJBr7NemumHSZVvEaPMN9yGm7iFaeI3K2hqqLM6E9wP0wB4aE6cK1d9fsVtQIHwqChCjwXErttTfx2u/6kodD4EO+a57yqLHuA1KtRVBwsmWYieCvsI+lTHJC9SuUva67JkNLmAUs0xG9SHq+RJW2LRCnRfXDeCVwTJWxqLMJsKuSJ55bWvC7Vm+oLj1yipBeaU7eFeKzJ4RXFI8prtVV1x9bMibGL6YQC+dG2H8ViAB9qoD51wZT7Jy2k5g3OcCZtaXiwKg0lnJb1NqPilKl7qfJ2YlRx5J4pYCyTDPl5UrzCRfcPd36Jf2AXXXVJYgcac76W4zkq65DzKf3eVW8a4u9AVG8l5ItIGPlQlL5CgUK3j96gEOm0y2kgtCGICzhM27ZlvSfLSRY2XtCXO1a63tS4vT8bQDdMRmXnxZhr7TzSQsDStQFXpsonRiuQ1lLzOf7+dovjvY1G/OJcji+0Pnn0MvEG49ULjWwujYbLlGrnKEDcZtYKz+hE4SPJkIbqjzWj5Gs2f4m6u0rAEO4EnNU/V8g2P5MlNV011qOSBWN/Kta2dsNkJm7kqY0ru22YeD05J3rHhI+dHsSx46aaDofs+UakZhJCLwHbBK01KXl92zTtfh5mHQfWwmi6x5FT+l3N5D6qrvH3cJE/a8HRPl4VBY5EqFu73T8Xxqoo7y2ZxSxAij/dgutcTPwexafjcL4k5xkNIhX2OKhS0uuE4QtoJ/GaVutchySunURSF6WTIcVjCiH6PfHDa3ZuSC5X7QnvDnVOUilXB6NIbCAeCV0B0OD1zYZyXbqQdp+HVNl7nXWuBhTHnhEYdWWR+6MEr4vOjBrqRPLEpEjSKlJ1Yuowa3PgCXPhOUFXqhskL13Z45sK4YwtRM67Mp7hdk5/h41Ty7EVnKCX6mxzO5AHoAWlB60LyrGpeKHk9kDxFUbgR/xAeDLj5Rdev7xl2kqQR6B53Li3gS+Eqet10Zo2rVaFwl/f1MfdKHkByHjW3zmjEz72hKXk5adesdVfxbIxdaCykl8YdjnnYHXnSrvnq6i5fN5dF+NrW2y1fcmHSefjKC9f3r9eBKHlagZIa6bxwbwrSsGH3wnVz/+wUxP2xoeSNdJgJ9wZg7glYfb7xLTthc6iWTW2/Y7K7XXMUSvt2TcBZwqYkeXf0MJYFZ8ecb5aMJ0KsWNMY20PqypPPnUpDyXNwLwwlGteLk668u7kqo0oeb2yiv/f6LYRTkndMuDyXJBrw8sIQZtAqukEIuQg8bNf0R0D1Ns3kBdgt6b1FSpumeBiNi6LMlZ2iM6smNEjes9GN41fyZP9MrRvJCxxU8oI+DxfPxHjLTfhKcQeiQrnaKTSle/lC8LH/Ce58Fd774yMvG40EHJHtX/v8db7n146qgT3BqOGljtHtvIDYeZcPlZlkyJGSZxl1xs0dqtE5d+9Lpkie92WGPJMnHgh509/9geINiPJuubAFYTEs6QZfvd19MN00DKK773I3dJErUjF2hOQCZ5RdcnnnszR9Qy9Rw0uw23yYJL5xSs7cAWtfAS3XmMd7Yz1LvlpvWF/d4spckixRDNV/vEqe3CkWds1695LeJtRtW1AXkkc1R8Hqza4JUJ58hCJhuPG5nl7fE+R9NmsE23ef2ZBx5bMBsZC74cSymRXzvbfNCeZ6VPLIrjEZCwyv6Lqah0CcYtV51QZjy0KZq1VYGo+QKenkutnWc5LkyeCVV1YyvOu5JL63/lrLl9gJm91qFCzL4vlraaYS4vNfHAjJy3cP6LE3pJuCPkYjAVSluzVxR863T/qqgth0InkAi0/B1jcaz7j5k+jK0wpU1BBetUv9CAiSp+XAqHEm4UbJE+vPG0XhtjrndL0GTMREjYK5M1wlr4r494W6qZsgNhortpIX5k6XrrzNbJlR8gSTLt1G38I4JXnHBJ9H5cml0aEUgJd1g5Aib4KHLVaKcqB/Zizqp2ZYFHqJRs5viIXx+AUqusHdXNVRHC8gZjKS8zweuM07d/Pdj+8Huk3yHNrNmhbuD8nwFcckuLgF0QlM0zra03P5x2D8Enzh74kkxyakIs6UvOev75Auamj1Hoq3D0PeRE2nSp5cmM44VPKK6XV8ioGVcEnyZCH6WU96yLNn4nzkDb+zxXUgvt9nBnz4/Bg+j+LIsvnSa68Qosrc/U+6U60kAfbkN5y/pl/IWomuu8seH3VvmLhSdqY0Xv0TQZSXPgbAl67u4FEVnjrfG8mbTYUYjwXZ84wPRckzAwlRoeBCyTPDzkiequcpOEmpbIO50RjPGQ9hXf9cS+fAsUDaNXfrQcLd5hSlkjfhEfdmR+ErMllzzXJZhG5DduWdj+nu5qz7gZZrzOQ5Jnmj5wELdlf2Eza7JXNn18U5lc/7l1d2mTt7XrhHNl5t+ZKw38vcSIhrXZS8mzsl7mQrfPJBURY9kI5OrUCxm1IdTADKASXPoyqMRrt35aXlf5+oy/tAN5J39mnx9bbYQJ2Xs2q3h9mVpxcoKw7V+7B0BJR3SYV9+L2qQyVvF/xRru+K9Yfj9RpCFb1hzuDNiw2IY4dct5Vwo+QdtGtqdbNB+Fshs5shoNQJJE5J3ikGgI+eH2Vtt8zaMe8OVWsGSa+8ER9W8uCAb9nuyutpLs/e0Rm72Jgb6NqR14zphzlXu8adbOV4F/PyZlH3diN5B5U8EOEr+WqdW07/ZsVtiE6Sq9SoGVZjhgAQg8Hf8UuQuQFv/OsDLxuJBLoujPPVGu9KQjyQWQGpXDnqaIuMiR1R02QmGSJd1LvGWGfvXgfAN9ZhHqIVQinwx5hh2FUB4hrO1h0oeSCul6YNgWjAy6MLI1378izL4tWXhI3uoceedvceZal8oDREklcrUyboSLEyAwnilLvPDFqW6MdbfFqUmwNfurbNw/NJEg4jvQ9DURQuzya4ayZF99VxQe4UV31iUyjqInjFExnBQO1O8rSCUPJ6sGsCnB2N8GfGB1GKW7D5Vk8/wzUkydszHHx+PD4IJgjqWSbjAWdKXoPkTfY8kwdwIbg3HCXPNGXwSpxCte7OrgmQvrafsNnNsplda1g1d0s6V7cKoh9v7jHYaK3kASxPxLjeRcmzrZrf+9AZYHDBK3kr1Pk6UT2C6B0KKXLSlbdT1PB5FKJlOavYjeTNPAy+SMOyGQ14GY34h2zXLFCmyzmx0ZR4rSgKk/GAszlTWYR+c6fIVKJLP+4hTMQC3LSmRdZD+rrj1/UM+TwuWpLkObnPHlDyuidsVnZFEBORU7vmKQaAjy6LHerj7oYr6wZJn7wRtyJ5odSBCgWgsSgzTYvtfJXX1/b4L2/d4zeeX+FffeVWayXLHsAdv9jYaVxyMcjL9BXi1bskKB6vZVPaiMzD84mHETg4kwdCyQOczeVZVkPJs3ePDih5IFIEpz4Ir/z6gR32kYiPvXJn2+zXbu81ht8HUuYrg0asbucFhF3TMqCaZdphV15pS4QHRCbPuXtfigKpBSbN7a7nZKBokDyfMwUlEG8sbG08c3Gc9zcLHReRr93aI7L7Dobqxzt5n7v3KFXOeGWYSl6RshVwplgFE8SVEulum0bp62JWpqk64Rt38jx7sb+H7bmJKLdrCazCMdo15aKzrIq6BjdqWzQUYNeKYRU7kzxPrUCe3oJXQCgRz5mXxf+5PqQqBbmZlq45SKcFGR6R5vxElBtOEiR3V9HVELtKvGFRcwVJ8ha9GTIlfTBuiE7Qi4Al7JpanZhjJU/eL9M3mB8J41GV7kpebr3x73t1Vdgbn1gcEX152TVR+t0Cy5NRVtLFjvH7z1/bYXEswn1TYhN0UDN5edMBoWlyHdmYiDtT8kYjARQZYGNvjrWFxwcLH4LV/fCV+dHwkO2aRYpO1Xtpd24OX3E0Z9ogeSVXVk0QJO+GJefrhxG+IjehS5ZYQzk6L6FUk5LXvStPtz8X9vn8NsApyTtGnBuPMJUI8uUbzhP4ekFZN0h4bCWvhXoVSjbZNcUH6H/8g7d5+le/yMW/+1ke/+Uv8IP//CV+8fe/zj/4zHv8L3/4TutZwvRV0WUTGWNV7jS6GeQVthRR7vyNu8dI8uTiw/R3uam1UPIuTEYJ+lRnCZvVHBgaRCYadpEjJE9R4OGfhO134N4bjW+PRAIYptWRvL2ysj+bMBAlT56XrqmJsJ8+Vc401Sh0fqjUM7cwLYXUGZdKHkBygZHaPWqG1YgfP3bIh0q27ndWxB2IHdgQgP3o/06WzX/x3E2ueG+jTD4oFhduEJ2kpgQY0e8NlfwWrUD3OSvAE0qScDKTd+2z4usF0en2/DVxf3FbnXAY58ai3DVTQsk7rvMj751FVdxP3FQoxIJe0lYcs7jd/qC6hsfUyfcxk7cwGiZNgkz8frg+pLk8qWpnakFn7zs8BqU058ej3Nwudr+e926R9k1xJh5y15FnQ5KgGcRnczt/zAm1tspv2zWdKnn+CMRnIXMdn0dlfiTcOWHTsmQRuvj3vbySIehTxQblnChFb2fZvDARo2ZYbcmMVjd4eWWXp5fHCPk8eFRlQOmaObJmsPucVXikjZLXLV1TjkrsrkJs6ujYSiucfUqsaeTCf34kzNqwlLy6DoZG0XL62dlX8gDOJELO1OlSGis8ysp20Z3rCkiF/awr05iowyF5dhCaKYQIR+cllIR6FWqVpk7f9n9DsyDvw6fpmqcYBBRF4SPnx3jxRsZ5ZHQPqNYM4p3smk27HYtjER6eTxIP+vjgXJKf+egSf/8HHuC3fvpRPvvXnuJrf+fjTCeC/F+fv3b0IbxzrWEtWUmXmE4E3c2QJGYBuBwr8M6dY5zLk0qe1al4FcSC2xc+YMHzelQenE44C1+xF27RyYaSNxYNHD3uwR8Wkc2v/17jWyMRsdjvlCb56mqmcaMbyFxEIxrfwY5eZD/63b553sl2fgCquTU2STE5knD/3pLzxKt3AcvRrOJAIB8qu7rXoV0zceBaAbh0JsZkPNDWsnl1s8Cfvb/FQ97bqNOX3b9HVaUYmmaGrd7maHuApZcoOVTyPOEkSdWBXfPan8Lkg42F6ZeubvdcndCMpfEIW9YIiqEdCGwYKKpZ8IYoGuJ8OC1DB4gFfWSs+P7iouXPF9dUoQ8lbzIWxO9VeS/6pLDrHde5aIZUtUs4qFCAhgX8/ESUolbvbjfbW2WDM71ZNaHRlTduDKkQ3d4ACsYpuZnJAxG+YidsjnVJ2CyloV5p2DVfWd3lkYWUIMJTl8Xc63obkjcp1Oh2CZtfu7VHpWbw1LJIvI0Fvf0reZaFpRVEQE+PSl66qGN2WEOli1pTfUIXq6aNxafEV1mlsDAS5m624jgtuS/IZ0/edPjZOUzy4gE2c9XuGyXlXar+JAWt7lrJU1WFeDRKxj81nEJ06awpGAECXhVPp/orGyE7vThNRFpu2yl5NcPEV5Vp96ck7xSDwlPLY+QqNd45RuWqrNeJNZS8Fg/E4L6SFwl4+YNf+Aj/4ec/zD/5sSv87U9e4ic/dJbvuG+S+6bijEYD/PzHzvP1texRm2n6KoxLkrdTZNHlzpD9ULocLwxFyXO0mxdM7KfnSTw0m+Sdu7nupbGNIvSJhp3kiJIHYrFx6fvg7X8vYsERSh7QdnFc0Q3e2sjx1LKwFeQHoORZ8ibqCTo4L43AiDRnEkEUBe50UfJCxQ02lcnedt1TC3iNCiMUhlejoJcBhd2ax9mD9lDwCuxXKbxwfafl9fL/PHeTZf8uwXpBLMB6gBadFYXo/fRbuoClSZLnkPgm1UpnJa+8C2svN6yadcPkhevpnqsTmrE0HmXTkql8x2XZrOw1kjUBV3Mt8aCXNAmxMG8H6SToZyZPVRUWRsJ8mQ+KTr+bQyhG14pYKJRxeK3Irq9zMuWx41yeZcHeLW7We6xPsJGcJ6GJec1jD1+xHSEBlzN5IEneDbAslsZFV15bUtNI1pwnW9Z5fzPPE4uSBHgD4j7TZi7v/EQURWmfsPnc9R18HoUPnRM/T5C8Pp89tTKKZVKwHIQ5tSB541Hheun0XNgpaPv1CSmHTpI2Q8XlAAAgAElEQVQzl8XIhpzLmxsJY1o4rgvqC3KDJGcGnX3mQ/vBKyBqFLS62d3hU86wZ4mNNLckDwTB3lDnhqTkifVJznB4P4F9Qr97ExBzee3mKkURunx+n9o1TzEofPicuJiOs0qhrBvEPPIG2CpUI5QUi1PT2UzCjz46y1QiyK99/vr+TlEpI3aRxi5iWRYr6ZK7eTwQVgxfmOVAjtuZ8vEVj9okLxDrfmyLhfvluQTVmtk1appSk5JX0PB7VeLtHuxX/rJYBFz9LwCMhO3ZyNbn4PW1PeqmxXc+IIbfB3GualXx7/EEnCh5tl0zjc+jMhkLdp3Ji2t3yfqnentzcvZsTtkeXo2CXsLyR9DqlrMHbfDoTB7AMxcmyFfrvHlojnNjr8wfvnmXn12Wr+mR5NUTohB9WF15pl6iQsBZVUAwQYxS5/d24/NivvPiJwF4cyNLrlLruTqhGSMRP6WAnOs7rvCVShZCScq6uH+6UdtiQS8ZK4Fa6XD/1yTJI9SzXROEZfP50rxYKA/DsqkVsPxRLNTuPXkgE3vTLI87IHmFTahXea862jfJC5VEIfqxh69Ilb/mi6HVTeczeQCjy6AXoLjF0ngUrW5yN9fmftsoQp/j1dVdLEvO49mYfQzuvi56KQ8h5Pcwlwpzbbu1kvfCtTSPLKQaGxmxgK9/+7wbpTqUgspBFXpC9sK1m8szTYtMUWcqZEJxs3MRejM8Xlj48L6SNyo2P4di2bRJnlNC4/WLtUrDrilrFDpd07UK1Eps1cW/y61dE5rm8jI3HK8fe4ZuV7I4nPGFptAiEQxj1yi0wmauyqiSo+ZPuh+b+BbGKck7ZozHAlw6E+PLx0jyqjWDmNrFrol1YPasEwJeDz//7Dm+entvv8w9LeX68YukizqFat39TUNRIDHLjCrOxbGpm5pNZhwqeYcseI7DVxp2TRG8Mh4NtFcmFp8RSubrImVzJHowAOcwXlndRVXgL1wSC9hBKHl6WZwXnyMlz7Zrir/VdDLY9uYJQF0nZaQphWd6e3PSxjen7AwvYbNWwpIzrM6UvJi4VsyDit1Hz4+hKhyxbP7mC6sowHePbImuyon7e3qbamqBhFImt9vB8jdI6CVKlkMrdjBBxCqzW+xwbVz9jNg0mH4YgC++v4Oq0HN1wpG3MCKvuWNT8rKyCF0sdt3Y8Gy7pqdWbB9DLhfBFTWK19P7I3l+JMKtPQ3r/MclsT7mGU6tgCE/P442BMKjYNYY81VJhHydSZ5M1rxtTfRu1wRRiJ5fJ+RTj78QXf4dy3J207VdEyB9rRFz39ay2VSE/vz1HQJelcvN3Zuzj4k5pc32peit7Jo7BY137+V5ann/cxkLesn3a9eUhKbYLV0T5GhJ7gChsN0x7RI2s5UaddNi0SPvj07tmiAsm7srkNtgQdYorGWGUKNgjwoYAeeEJjxyIHgFuqjT8tgNTZBr+zVuMB4L8G7tDBh64zN5bNBLoPoo1BTnG2mxM+CPNpG8EBvZSksV/G6uypiS26+1+TbBKckbAp5aHuNrt/eo6MezE1LWDSKKXYbeYgEflA+AqoMwEYkffXSOM/EmNc/2ZI9daAyFu+lcaSAxS6ombsbHNpcn0wGDAX/3Y4PxI+T37GiYeNDLm93m8opboPoglNq3i7SDqsIHfxxufhFyG12VvFdWM9w/HWckIjrcBqHk6RVJ8kIOFE6vX1hZJMmbSYXb7ywD5DdQsajFXXbk2ZB9cLPDJHl6CUtuijjuycPaV4olEmEfV+ZTB8JX9ko6/+61dX7ggzPE9t6B8fvA10NCIOAfF4uWWnq1p9e7hVIrUXY6ZxVMoGJSLrb5LOc24L3/DA/+kPgMYFcnpHquTjiM5MQ8JsoxKnl7EEw27JpuevJiQS9p5NxhuxoFWwHyurdTNePsWJiyblAcuyxKkIutExYHBr1AXb5nZ11fYnGlyLm8jl15e+Jav21NMDvSn5Kn6EUuxGvHP5MnFdkigiy4sfXuk7zrTTUKbc5Pbh0CCareGH/0xl2+64EzB4OjGuErrS2by5MxVtOlIwmbdnVCcxhSLOjrfyZPXt95Qt0VX7sQvemZbNcStVPy0nIefsZ02JHXjLNyLm/1BcajYhZsKAmbdv1IPeDcoi3tziDsmtBFnZbHrpYDLI5FUJ3MuB3CeCzIm1XplDhuy6ZeBn9EdJE6PSeKcmCedTYVQq+bjWuiGZu5CmNKHk/s22ceD05J3lDwkfNj6IbJq7eOZxi+UjMIqxoontYytH3jPOR174SgT6h5r97a5SsrGbFT4gtDYq7x8OnF401iFm9hg+lEkLePqUbB0oqUCDpLTAwmWs5ZPTSb5M1uCZuyIw9FYaegtQ5dacYHfxyw4I1/Q8jvIeTztFTytLrB62tZHj8r1LREyDcQkleXdk1/yOHfLTImFosIJe9ettp2TqSyLaKrVUnWXCMQwwqNMK/uDHUmz/CIBZkju5mdxqodJTTPXhjnrTs5MvLh8jtfuUWlZvBzTy/C3Td6tmoCRM+IVFpz73bPP8MxLAu1VhZzVg4rFADq5WzrEICv/HMxI/ahXwT2qxM+dmlwPUULk0kyVhw9e0w1E1Wp5Glik86RaiURC/pIWzKIqB3Jk/efut/B5ksHzI+Ia/muT6jixx6WoBUaxNRdDPxuI2GzLfZuYaJyxxpnrk8lD+CBUI6tIc3k5S1BSmNuZvJi0+L5mr7OeDRALOBtn7CZXYfkHH/6zib5ap2/9NihjbXErPh5bcNXotQMi1uHfv4L19OMRvzcP7UfhjSQmTx5vyxaoe6JvS3WKvtKXuu/334RulTyndo1QYRBhVJw6wVUVRlewqYkeZlawLlFuwXJ28x1CLySx75f8Pe2VkPaNU3plDju+4leAn+Eim64m00eXT5g1wRYb+E6uputMq4U8Ma+fTry4JTkDQVPLI7i96i8eEx9eRXdIIwuHhKt7IIhqeRVnCt5AJ9+bI7JeIBf+/x1YdccPQ+qykq6hN+rNvrTXCExB6UdrkwFjy18xagWnEcTB44qeQAPzSa4ulXoXABe3IKo2BVKF7soeSC6e84+JYrRTZORiL+lkvf2Rg6tbvK4nLNIhHwD6ckzpI01EHZ4w08twPb7AMwmQ+hG6x0ygMKmGHwOTrjYRT0EJTnPojc9xJm8InWveCg4Dl6B1nN5F8exLLFQKut1fuelW3z8vkmWw0VBlPsgecFxsWjx5YdA8uoaimVQtpyVodsugZBRPKp8l3fha/8SPvAjjcX2oKoTmrE0JsJXqpn1gf3MA6jsyZk8qeS5Stf0krFsJa/N/V8bDMmzZ4oai7Lj3nnXCtQ8Lj4/jYRA0ZWXKentP+u7qxQCkxiKt7eOPBvyulsO7A4nXdPjp1AX10c04EKpVlXxfM1cR1EUFscjrLQleWuQnOffvrrO3EhIlKAfxtxjbWsUlidkwmYTyTZNixeu7/DR5bEDis9A0jXl/dLZTJ6cLWwieZGAl4jf01bJs5Otk9UNoRYHXaQ7qyqc/WijL29hdNgkz+9ctWoieX6vymjE3/maLoljr/ZJ8vJEqIXGh6DkFYWSV6u7S20fuwD5DdBLTYXoR/+Gm7kqY2r+2ypZE05J3lAQ8nt4ZCF1bOErZd0gpGitQ1egJyUPhJr3c8+c45XVXar33tuvT9gpcXY07Czi9jBkwubjY1VW06Vj6UQztQIlQoR8Di7vYFwssg6pEJfnkhimxTt3O1hKi1sQnaRumGRKeneSByKAZe8WrL0kSd7RB9crstzWJnnxkHcgSp6hlahaPiJBB+8TBCHdfgeKOw1Cv9EmfEVLr1KzPCQmelTyAFILzCrpIc7klal7pF3TDcmrHr0mHpxOMBLx89y1Hf79a+vslWv8/LNLcO9NcUAfJI9gghxRgsUhFKLLmo0yDm1EckG1GKnxt//jWwctYK/9JtRK8JG/1vjWoKoTmnFuPMKmNYJ1HHbNuibOSShJUTPwe1RX6bFRv5cMctHZritPbjI5qjbpgJlkCFWBq6Uo+GNDIHlFNI8glo6uFVvJy9/h/KQMX2lnSdy7xbbnDFOJEL4+5hRtkrfgSbOd1zrG8PcNLS+K0O3ZTTdKHkjbmVAkOtYo5NbJB87wlZUMn350rrUNr0Mp+rlxO2Fzf7Pq3Xt50kWdp5cPLoBjQS9Frd5fR2d1X8lzlK4JLWoUgm1n8mzyFymtubNq2jj7tEgs3bvFnFTyjr2T1O6Ec3JObEQnRSCRnFecjAcd2TV3zVhPoSuwr6IWYueGpuSV3Sp5Y8LpQuZGo9O3VY3CdrZA3CpA5FTJO8Ux4KPLY7x3L992N6pXWJZFpWYQQmsdugI9zeTZ+LHH55mLWgRLd2D8IgAr6aL7ZE0bsivvoWgey4L37g1+Ls+1XdPQxaB6Ey47CV8pbkN0gt2yjmW1qU84jPs+JRZgr/8eqYif3fJR8vbK6i4XJqOMRMTc3qDsmpZWdB53DiIsBuDW802F6K1JnrV3m3vWCFOpPhaqyQXOmNtkS8e8425DL1OTJM9J8Xcnu6aqKjy9PMbz13b4jRdWeexsikcWRiTJU+DMg3291R3vGeLVO339DEeQi48SQWd9cJLk/dUnRnl/s8CvP78if04JXv6/RW3CpAicGWR1QjPmR8Nsk8Jf3hzYz2zAdj+EUpT1OmEXReggrgvNb3c5tbdrlpUwAb+DGeIO8HtVZlIhbu9VBGEYgl2zqrpQ8uKzIt7+G/8v57slbO6tsmZNNu47PUN25U1Z2+iGebxW8GoOgvGG8uUqeAWE7Sy7BrUKS+NR7mQrR50klSxoeb6WjaIq8MOPtJmBnn1MfG0xlxfye5gfCXO9KT3a3oC2K3tsxII+DNNqJMv2hIaSF+o+p9iG5I1HAx2VPL9HxZNd7Y3k2X15q8+zMCLmWtPHXVfT3DHp1K45sghmDfLiOXAmEewavGKhkiPau5InbaHp0KK4nxwn+dVL4I8KZ5qblOGmhM2wv31XXjVnF6GfBq+c4hjw0fPiwnrp5mDVvGpN7JwHLK11Rx402TXdKXkg1Ly/+bC4TK6bU9QNk7VM2X1Hng1J8s75xeLpbSel4y5haUVKlsP+mTbqzJlEkIlYoH0pumkI+5WsTwAYjzpYpPnD8OB/Be/+J6aD9SNKXt0w+dqt3YaKBxAP+gZShm7pZcoEnQcCTH9QhK+sPNdQ8tolbPrz66xbE31bq3zUUI47MMKGXkRXxft1d620viaeuThOpqRzJ1vh5545J755703xEHLS2dgBu/5pRvRjChZphi6UvIoVcEZoJMn74LjC935gil/7wnVu7hRFimxlFz761xuHDrI6oRkBr4dKcJJQPd8+wbJX2PdMOZPnpgjdhi8YoaqGOto1y0rY2VxoFyyMRLiVKYsNuSHYNSuKvUni4L2rKjz8U3D7y8wYdwj5PAeIxv7PLUJph2t6n/UJNpLzTJpigff12+6fgY5RlUqedKe4mskDGb5iwe5KI9TsyFyeTNb8kzt+nr3Y4X47dVmEgnWwbDYrec9f2+HSmVhjUW/D/jf0Zdm0Z/IIdx+haEfy4oHG7N1hpAs6M1EFJXfH3Txe44dfEha+1ReYtxM2d485YVMrYPrCmKjOrYmps+KrTLl0ouRVvXFM1N5C8kAUzAN3vAui4iN/TAnG0LBrlrS6u9CikXOAcjBh85Bds2aYUJabbKd2zVMcBx6cSZAI+QZepVCRO30Bq9qe5HkD4r+5nMmz8T1T4ib96+/5WN+rUDctlnq8aRCfBhTi+hYTscCxzOUpuhslz1Y5W83lJY90nzVQ3hXdX80kz4mSB3DlJ6FW5sPa8+wdmsl7916ekm7w+OL+nEV8QEoeeomy5bD/DED1yHmF54kHfcSC3rZKXqRyl23PpLNz3g7yIRauHOODpBm1MpoqF6lOe/Kg5UwewNPL4yiKCDb42EVpCbn3Zn9WTYliaIYJc+tIfcPAIQtpSzivUACgmuOXPnU/Qa/K3/mPr2O99E9h7kmYf7Jx6JeuDrY6oRlKXPYzFgZMhO3FpkzXjLhU8kAmFKrJDkpejiJhd7vXbTA/GhYR8GMXxLlwWJvjGpYFeoGyYgcXOXzvH/wJUL2or/8OS+OR1nZNuYh9pzraX32CjeQ8Kf0eiZCPP3nnGNReG1oeggmKvSp5LRM2D5GNnJg7fbec5NOHA1ea4QuK+85664TNC5NRVtMl9LpJWa/z1du7LedkY0ExV1jU+nj+aAVqnpAkNF2uE/t+Uj4YUjceDbS3axY17g/tAlZvSp6iiNGEWy8wn7JJ3jHP5WkFTJ+LZFrYL3nfFcmzZ+JBMiUdrd5GZS2nyalxZpIuLKGHEPB6SIZ93FTs8JX3e/o5jlAT6ZqVmku7pi8obNkZSfJGwkeUvINF6Kck7xTHAI+q8OFzo3z5Rnqgfm87DMBvdbBrguyf6Y3k+fduYCoe/tNaiH/3mnjI9OrxxhsQ3vLsOg/OJI6lRkHRi8L/36cF7/JsgpWdUmuCZatN0YkmJc+hijX7KIxd4LG9z1DU6gdu0q/KebzmcttESBTS9jtPorhJTbSx+LSIM8+uMZMMcSfbYuewViFWz1AITvf1/uxC9KR2F+M4Z2ds6CWq2Eqeg3MSkMEYLa4VgNFogP/5e+7jl3/wA2JOprgjrDUDIHlabB4/daw2XXBvbWT5wX/+YufuMSeQds2ym+AigGqOiViQv/O99zO59hmU3PoBFQ8EyRtkdcKBtzEiZq/M3IA3CKr7ds2S7jIQQCIW9JJVklBqM5On5ck7CaVwgIWRMHvlGqWEVJLl7vbAUSuDZVJUwvg8ivM5xdgkXPwkvPH7XBrzt07YtDvyzImBKXlKbp2PX5rg8+9uHakOGBiqeQgKJU9RHG4cNWNUzhalrzcpeYfOj1TyKuHpRodqW8w93rYU/cJkjLppcStT4uWVDDXDOtCPZ8NW8vrqyqvm0DwOU1g9XuEeOTKTF6Co1RvrnWakCxqXAnLzvBeSB8KyWbjHPHdRlQ424kFBL2J4XXS0AsRnRN+qrBc5k5Cpo/k2I0DlXTJmjHMT/c36TsQCvFuTz/bjtIDrJUxvmJphud/wGrtwoEbhzt7BrrzN3CnJO8UQ8NHlMe7lqu1Ts3qA3b3nM6udLWHBZM9KHjtXIbVIIhrmN14QMzc9z+SBsGzm1nlwOs717cLA+wNV2fPlJjiiFQF+SBbMfqNV1YNN8iITjXSvsZjDmRpFgQ/+BFP5N1lS7h5Q815e2eXsaLgRkQxCybOsPi0zgFKvUHG6eLexJOfyVp6TJK+FkicXHtVojx15NpLi9TPsDEa57ATThFqZiiLOs6OHij8KitoyeMXGX3lqiUfPSoK+OYDQFQkjIUhMZedoV95bG1n+8m++wutr2e61H90gg1cMb8hZsJLHK2ZMpWL0I4/M8DfCn+G6NcfWmacbh+0UNN6+kxu4VdNGYlKcn+zW2mB/cJNds6wb7tUZZMImifZ2zWqevBUmOAiSJxM2NzytaxQsy+IL7231v4i1Z4rc3k8AHvmvoZzhE56vcSdbafQPNtDckTcgkode5PsvBMlX67y8kun/Z7ZCNQeBBIVqnWjA637u1B8Rc4sZMVs0lQgeUfLK26uUrQAff+T+7oE0s49BvQJb3zjyn5Zl8M21rQLPX0sT9Kk8ejZ15Lj4QOyaBTRPBEWBoJMwtFCy5UwetO7K2ylqLKryedwzyRPPOf/al/nATKKx2Xps0ArUvC5Ci0Dca5PzB+yaQNuETauc4W4t3LvrSmIiFmS1EhLpnsep5OmlRlqva+VxbBkyN8E0mU2F0Q2zsS4DUYQ+qsh1XPSU5J3imGDblAZp2bTtmj6j0kXJ64Pkpa+hjl/gZ59ewjAtUmEfqUgfIQHJOcht8MBMAtOC9zYHqOZZFp56mSJBgt7+EhMfmhEEsKVl007Ki05wY7tIxO9xt8t/+S9hKh5+xPMcGTmXZ5oWrx2axwOh5AF9Ex9PvYymBtyloo5fEmlUq88znQy1tmtKkmcl+yR5vhDVwBhzwyhEl2SmgouZPEURal4bJe8I7GTNMx/o5R0egCqtrBVZVWHjrY0sP/GbrzRmGPo+b9KuaflcLAyCif2EyBufY75+i183P8Xf/cN3G66F52VR/LMXjyfZ7P9n783jJDvv8t7ve2rfu7p632cfzSZrn5G1WJIXCctLbCAWyNjGwhjIJVxuQpILhHsJgST4Jg6JsUMwxgu2McYY24AWy7stWZu1jqTZZ3p6Znrvrn0/94/3nNNV3VU9dZbqnm718/noU6NT1TU11eec9/39nuf3PL2DUsq0MOlwzIRhvCLlmlbYtojfw7QaWTUMfaHaQoZYCxjVZopOlBJyJmtmqcg7O5vhvZ98gg9++in+y4M2N2sF3R2wRVlvLbbfCbERrp/9OoCc4azF/BmK7ihJwvYy8nRoDptHEhmCXhf/9GKbJJu6XLNQJmKhGQBIp0CNkdjeHeLksobwxXPHmVC7+NnVpJo69FD0o1+DSy/A9DEp9UteYEcwT1RkOXlxge8dn+bw9kRDqb0eA2ErK08z6Al4XK0VvsHOhu6asLLIq1RV5jJFBquX5H0osLJQbQmd22W24Jnvc3hHgmfHFxxvPtehkDaKPFPXT3ybIdfsj8n9XjPzlWp6hulK2BEmbypZkHuBdhV5qgrF9JIRmtl7StcuuaYnJxrGKFxazNEtkqgu79Ke7zWCdSvyhBB3CyFeFUKcEEL82/X6HGuJkUSQ4c6Ao1EKuuuVu7LKTB7Im58F4xUqZdkh6drN/YdHSYSsZ64YiA3B4nkOajbqLzkZil7MIFA145VWIhQ0Jq/Bxj0e8jKaCPL8eHMm77Mv5vnKMxO8/XWD5j5npI/Fwdt5l+v7zKdk4XRsKsVirlQ3jwdLRZ5d8xV3JUdRMdkZF0JKNk9/l4GYn8VcaUXsRWlGLjrehIWh92UoRIYZFlPMtzsQvSYqQAjwtSo388WazuStwMXn5JyhbnxkA/6uUaqqoDhzyjj23Lgs8DqCHv7mw0fwuhRmGkRymIJW5DWNY2mEQMfS7NcP/hvEhtl95y/w8NFJHtQ21N85Nk13xFcXtOwkxgb6yKg+57PycvOAAF+MTNGkIYCGiN/NZCUqmbwGM5VqfpGFqt8RuaYeiH5mvgiJHTB9jHKlyv/67kne8tHv8ez4AoMdgYZhwaag3S8XqwHzn1szYOma+hEjogGrOHeaOd8ALkXQb8fISYdW5PnS57ljbw8PvzTpvBy8UpZSZ3+UdL5sPj5BR9dumDkBqsr2rjCnp9NGo0RVVcpzZ0gH+tneyhocHYTYCPzgv8InboGP3QB/8jr4r1fh/+hunvc9wK8+disdMz9pKNUE54xXsiLU+nnSYK+iM3nL5/Lms0UqVZWe8oQs1Ky69gohJZunv8+RbZ2UKipPt9OkR2M3waSsNz5mMHl9WuHb0HxFVRG5OeaJsMPqaI2G7oh0NlW79sgirx0Om+U8qFVjRt707HNCm2edPc5wgxiFCwt5el0pRKjb+jmyQbEuRZ4QwgV8DLgH2AfcJ4TYtx6fZa1xy85uHj81S9mhuQC92+S6XJHn77A2kzd/Wtr2du8h6HXz6V+8kT/4Z/bs4IkNQ6VAvydNZ8jLi07O5ekzRS0br6zumHhoqKNxjEJmmpIrwO/+0xnesr+X//CO/aY/av7Az9ErFnCf/hbQeB4PliQzdpk8dyVHSbGwadp+O6Qn2eOW807L2bzs1EkKqodI95CtzwegxkYYWgsmT48KqPoIttphBsnkrSLXrINDpisAnbEIF+k0Fvjnxhe4/5OywPvih44wFA+SCHuZtWv9rRV5pjLbdCbv3ONw7jG4+f/gA7ftZv9AlH//tZeYyxT53rFpbt/d3TjTywF0R/1M0YnqtPtbbl7++xSFbKFi2XjlYjksjZqWN9pUFfJJUqoz7pohn5vuiI9zs1no2k3h0iu842M/5I/+6RVu3dXNI795G3dd1cNEg7BgU9AaHclKi3mKy3HNz6MKhfvc31lZ5M2f4ZLSR1/Uj9tORp4Orchj4Rx37+9jJl3gmXMOb+D1JqHmrmlF1gvIzWoxBelJtnWFSObLzGr3wsdPzdFdmSLWt6O19xIC3v91+Lkvwc9+Ft79SXjnx+Hej8I9f8yXO38JD2UOKy9z++7GtvJLRZ49Ji8jTJh/NCjyeqKN5ZoztUHoVqWaOsZuhewMN4ancCmCx061J9cYgGLKKGhMyZ07t8l9XG6eaMCN36M0ZvLyiyhqmTk1Yrsp3x3xUaxUycd3y/t8O9yvtXXH0ncCdTEKgx1yH1xb5F1azDPgSb/m4hNg/Zi8G4ETqqqeUlW1CHwReMc6fZY1xa27ukgXys1dG01Cl2sq5WwLck0LC5s+09ElM/IODMbY22ezG6/FKIjF8+wfiPKCw0we0LrxymXmrK4einFhMc9Uqv5Gen78DBPlKLft7uZP7rvG0mbEt+8eZtUIfaf+FoAfn5pjIOZfMYeiG1XYLfK81TxllwX50zY5W7Uz/TSwMkahPHuW82oX/R32pVWuzjEGxCwLqTa7m2lRASnVb25z7Y+2JtfMzcuCzKEiLxHycq7aiyd5lme1Ai8e9PLFDx1hUIu3kEWeM0ye8Jn4XfpjUtb4g4/KuY1r3ovbpfCf332IuUyRD/zlk22JTqiFEIKUtxtv1uENSH7BkIClC2VLEQoGkwcrJZvlPKJaIqU6Y7wC0nzl1ckUP1hM4F48w3wyzcd//lr+7L3X0R8LMNgRIJkv21MGaE2SuYqJ3M1aRAcQu97CP3d/l9OTNWthtQIL5zjtlOkKGFl5LJzjjr09eF2KwTA7Bv2e4I+SKpQJ+y2aC+nBzjPHVjhsfvWJV+kUaYa37Wn9/eJjsPstsO/tcPCn4XU/B9d/AG76EKf3PMB4tZtrfOebFgIhrxshbDJ5+aTmHgeZoJEAACAASURBVNvitdOgyOsMenEpYsU6PJ0q4KZMMHvBfpGnrXPBiR9xaCjGYyfbNLsJUEiTE1aYvCWHTSEEfVF/45k8LQg9647T06rrdxPoUtnZgPZ3t0Oyqd1P8sKiXDPcI2WYM8cJeF10hb11cs2LmlzztWa6AutX5A0Ctbqa89qxTY8j2xMIAT847swNJFusIKiiVFbJyQO50JWyUDa5CdSzlnR7ZyegFXksjnNwMMaxyVRzG2CzqAkZbX3OKroqkwfUSTa/eXSSc+dOk/cm+F/3X4evldm/BoiFQ3y1cgvD099BTU/z49NyHm85qxTVNgxJB4q8itvCxik+Bh2j9Mz8GGCF+YqyeI7zare9jDwNvu5tuEWV4vx52++1KvRmQMVjbpFd5Vypw6UX5KNTTF7Iy1m1B0/yHO/VCrwvfOiwUeABJEI+o+tvGaUMJdz4fCbOE38M5k7CsX+Cmz5sSD0PDMZ44NZtPDe+0LbohFoUAr1Eik3m3qwiNw+BOOVKlUK5asldM+p3M0uTIk9rLiVbyRBrESOJIM+OL/ClM0FcQuWh9w5yz8F+476iB4w3y7xsCdp9dr5isklSi+veT6e6QPfFby8dS05AtcQrBYfiE3R0jMDCOcI+N7fu6uLBFy856nJtNAn9MdL5ko2ZvCVGQi+8Ts+kWcyWePElaaDiSYza/bSAdNh8RR3has/5pkoGRRGEfW7bxitp1SSTl1+okzYriqAr7G3I5A2KGYRasV/kxUfleXL6exzZnuD584srTYGcQm3GpKmZvDH5eLmsPC2CwhfrNm8AtAx6kXjBq5137XDY1PNZkX+XaXWAEHKPqu1XB+P1MQoXF/PEWZT+Aq8xXLHGK0KIDwkhnhJCPDU97fDCvY6Ih7wcHIzxgxPO/JtypQoBtI3danM0RsioSQZx5pgcSPY7OEsT04bGF89zYDBGuapy7JJDlsVaRyhNoDXjFViVnTkwGEURGJLNH56Y4Vc//wxDnhQ7tu+wnD8DMlbju95bcallpl54lJl0YcU8HjhkvFIp4aFE1UqRB7DtNnwTP8KrqCuKvEDmPONqtzEjYAfexJj8w4LDLonLUZJF3mLVJBPhbz5bVQfDdMWZIi/odXFB9BKrzNEfqPLFZQUe4JhcMy9abJDo8MdkA8kTghseqHvqN+7azVgiyE3bEm2JTqiFiA6QUOfI2cn0Wo7cPAQ6yGqKCatyzRlVm/1dHqOg3XeSatDWvaQWb97Xy/WjcR74Z3fLvz9db9ajnzdOFHlzJW/ruZvLsfONpLw93JX9J4pl7XrSTCVeyHY6x+SBUeQB3H2gj4mFnLNjAk7JNSMDslk7e4KBjgBet8Kp6Qx//9wE3VqguyE/tYlDQx28rI7QXRyHUvNzIer3WC/yqlUopkiqJmY3A3FQq1Cob6Z1R1Zm5U2nCowJjb2P258JZ+w2OPMDDm+LU66qPNWOubxyESoFMiLQuuOoDqPI02MUmjF5Umoa7ey1+WGX8n8nSmE59tMWJk+ux1nNCM2SOiCxC2ZPAHogujyni+Uq0+k8kfL8llxzDTEB1NpDDWnHDKiq+meqql6vqur13d2bi2J9/c4ufnJuYYWBhRXkimUCaDe+y83kgfm5vOlXnWXxQN7EPSFZ5A3IzY9joejazaKkBFqf/6lxB1yOoNfNrp4Iz51f5Omz8/zSZ55iWyLEkCeFJ2r/Bjofkt3HS6ePAnDT9s4Vrwl6XbgVYVNeJb+XqhnXxFpsfwMiv8htkQv1M3n5JP7yItPuXkumFCsQl91Cd6rNRZ72fSQrXnMB7jvuhNQFePavVn/dxeek8YFDds1CCNJBecv83Lt7GehYuQHuCvuYzRTsMRTFLDl85jbuunnRde+Xzng1CHhdfPXXXs8n7r/O+mdq9WMkhvGICufGHTx3clKuqXf0rRqvzKo6k7dszkdjgFIO5eQB3H2gny//ys0cuvp6QEhXxRroDFnDOJRWoRV5MyWLM3kALjcT297NreJ5zp/R2AGNoTjnVHyCDr3IU1XeeFUvLkXw4EsXnXt/ff2wa7yiKNIwZ+YYLkUwlghycjrDF58Y54YOzRQpZtPFWMO2rhDve+e9CLUKUy83fV3E77Y+k1eU58mCGYMeoyG9bC4v4m/A5BXZ4bIZn1CLbbdBfoEbAhN4XKI9kk19HlwbKTHFtPnCUnJYE4g+mVx5zy8kZUMg0d1v++PqTN50uqg5bC4xedlimWfHF/iH5y+SL9lQYxnfiY0ir2uXVAIU0gzHg0ZW3lQqT0jN4VaLW3LNNcSTwC4hxDYhhBd4D/C1dfosa45bd3ZRrqr82IG8nmyxQkBo3fvLuWuCubk8VZVhut0mZgBagRBGVt5wZ4Co3+3cXJ62+Si7TRQzvtiqZhqHhmI8fXae93/qCXqjfj77/qtR8vMy1N0mAqEO5pU4+cljdIW9DTNthBBEAx57TJ7mJrnqObIatHmFO7wv1xd5Wnc8G7BvugJAdIgKCsFMu+Wa8vtYLHvNLSiH3gMjR+CR322eewaOmq7o+NA77wKgp9TYXKQz5CVfqhqOu5ZQTJNR/QTNFDPxMTnbeuRXGz7dEfS2ncUDiPVIhmNy4tRlXmkCmlwzU5DfqdUIhXnCqEJpINeUTbeUGTlbq/AGZVzNTL28qivsxedW7Bd5ipvFkstWcSqueS8Alac+Iw/Mn6Yq3FxUO52XaxbTkJsnHvJyeHuns3N52vpR9UbJWMxTNNC12wix394V5rGTMxy9mOT2niy4vI6sOzpi266Rf2iQpafDllxTW48Xzch6A1qjqIHDZiMmb693RjaNww5I8bbdKj/CxGNcPdTBY+3IVNS+kxQWnGlBMpY1cs1iucp8tn5vMD8tz+2+AfvrctjnJuBx8cqlFOPuEbITL/Ern3uaOz7yHfb/3kO882M/5Nc+/wz/+IKNponhpSCjuazI4g0yYvYEQ/EAxUqVqVSBi4t5EuK1GYQO61TkqapaBv4F8BDwMvAlVVVfWo/Psh64biyO36PwwxP2byC5UoWYSy/yLmO8AubkmskLshOnzwk4CS1GQQjBgcGYczEKWkeoYjHnqxGuHu4gXSgT9Xv43AM30aNoFvoOLCqdIS/jYgB/8nTDeTwdsYCHxZwN5lcraoTXIpMX7oHuq7iu+kK9zEsr8kpRZyREuL3MuxJE8w522RtBO0/myyZn8hQF7v1vcqF++Hcbv6aQlps0h4u8vrGr5B/mVwaigzRnAexJNktZMqrPXGbb1ffBb768NGu7TujWsvIWncrKq1ZlEebvIFuU157VMHQVhaK3c2WRV1hi8pyayatD154VTJ4QgsGOgH25pjdMtlSxtiHTMLJ9L9+tHqL/1JdlDMH8GTKBASq4nGXydJZHm9m5e38fJ6czHJ9sMQ7lctB+j1lF3l8jVpk8kLKzhXNQyrOtO0SmWMHnVtjtX5DXmOLgti2+TRZIl5oXeRG/m5RVCbRW/M5V/K3fU5o0pLsjPmbThbr4i5l0gW2uSXvxCbWIDkC4Dy69yJEdCV6cWLTnLNoIepFXNSmL19G5VOTpc/DLHTbT85coqB7G+uzvUYQQ9MX8fOWZCT51zEewvMDFi+fZ2xfhN+7azSfuvxZFwJllmY6moBV5qaodJm9pnrU2K+/iYp4EW0XemkNV1X9UVXW3qqo7VFX9j+v1OdYDPreLsUSIc3P2HQRzxQodHm3z3wqTZ0auqXeAnWbywCjyQBo0vHwpRcmJWAktpFc1ZQG/umPiG6/q5Z4DfXzugZvkPIsRhG6/oxoPeXml1MtA9QI3jq2UauqI+t32jFdKumuijcyc7bezI/c8c8nUUgTIgtxQuzodKvKABe8AnaU2F3kaszlf9pg3jui5Cm7+dXju83D6+yufn3wRUB0v8gjEpexaW+CXo0vLkrKTlacW0qRVnzkmTwhnZ3Ytwt8pi8z8rEMscDElZ4MCcUNab6Wg0Tf8OW8c0o2NV6S7pgNy5+Xo2g2zx6VrZQ0G4wHO22HyimlUX4R8qWqrOA14XTwSuJtwcQpOPAJzp5nx9DuXkaejT4v90QyR3rK/DyFwjs2rkd2CtWaAga5dgApzJw1lx08d7MebnnBMqmlAUaB336pMXsTOTJ4+u1k2Iett4h/QE/VRVWG25v42nSowpF6ShY9T6NkL0y9zeHuCSlXlqTMOz+VpDcbFqs/atRMfk3uncpHeJll5+cVp5ogw2kAZZAV/9K6D/NG7DvLz974ZgK/+dCcfv/86/uUbd3H3gX76ov46oxPT0L6TVFUzXrHyvXRul07ps8cNFcD5+RyXFnN0Ca2J79D4xEbCFWu8stnRHfExbdfuHFnkxdzaDXg14xV9Js+MXFPvAHe1o8gbll3tUo79A1GK5SrHJx0wX9E6QqqDTF5fzM/H77+ObfoN0yjy7HfJEiEvJyu9dIskRwabS9rsyjUrBfm9uKwyeQDbbsdTLXA1J4xh78rcGTKqj0hnn/X3XYZ0YJC+ShuyeGqhnSfzRZc51krHbf8aOkbhH35zpWOtbrridJEHcoGfa8Lkhe0zeWoxQ0b1WzfTWE+EeqiiUHUqK0+/VwbiZAv2jFcAMu54UyYv6eBMXh26d8ug4WVGRk4weao3Aljsutdgsu8NzIk4PP1pmD/DhOijP+ZQRp6O6KBcA7Vipifq59qROA++5FSRtwDuAOmSZJNszSfrsrOZ41wzIlU/7z0yKn+HHQ4XeQC9B+T30mSWV87k2SvyZssmDK6aMXnhlVl5c6ks3eWLzszjGX/RVTD9KteNxPC6FOclm9p3smDVmTa+DVBh4dwSk7esyFMzs6SUmLl581VweHuC+24cYcf+6+WBZeYrQ/Eg43ayN7Wma7Lqxe9RrOWpun1yTZ45VsfkXVjIM+jR9pZbTN4W1grdER/TjVyRTCJbqhBztzCT548Bwpxcc+aY/DkntO7LoS9WixMcHHTQfKWYoiB8+LwmZoB8UXnjvZxjog49DNQhJu+0KgukXe6ppq+LBTy2mLxCVi4sLr+NIm/0ZlShcLPrJS4syHO3MHOacbWH/gZGIFZRCA/SzTzFvI1N6OVQzIA7QKZkwa4ZZEPlrf+fvEZ++Cf1z118Ti4mEftD7yvQua25XFPbBM3ZYPKqhQw5LGafrTdcbtKeTrzZSWfs8fV7ZaCDTNG68YrO6iRdDYq8fBIV0Xrki1noDTptxkvHYEeAmXTBullCIUnZYyHnqwG29XTwpcptqMcehPwCp8pdzko1QbLNfQfrZIl37+/jpQtJxh1Q1FBIgj9GSmN8LRuvACT0rLzj7OwJc/T/vZtr+wNy3elwJj6hDn0HZJNzsTEDLpm8krVrSnPINGe8ojWktRgAHXoguj6XV6mq+LKXcKtlZ4u8nquglMWfPs/rRjp4vG1FnklZvA6dtZw/TU/EhxAr5Zqu/BwlX9zuJ12JSL/cLy2LURjqDDA+Z4fJ09yuy257ioauXTBzAr/HRVfYpzF5eUb9mpQ0uOWuuYU1Qk/Ez3TaphMeksmLurTN/2ozeYpLSqrMMHkzx+QmwQmt+3LUZOWNJUKEvC5n5vIKaXIiYK6D5Y8BquEEdlnoTJ4DXaFEyMtpVRYDytzJpq+LBjy23DWLOdnJcvsjlt+DQAeF7qu5WXmRiQVtYzQvg9D7Ys5tysqxERShkppsXMw4glIWvEEZQWJ1k7rrTbDvnfC9P4bZmt+dbrrSjusmvk129CsrO+v6TN6MnZk8jclri3RwDVAI9JKozq5w4bOEGiZPN16xEobu0rLGFpWOhkxe0R1CRWkTk6cXefWbMiMrz6pks5Cm4paSeLvnyq7eMH9VegMCuRa+lHc4I09H30GYOmpIV9+yXzbXHJFs5pOGsyZgPScPwBuSzOOsLMwVRUjXQHBergmSyYOmks2I302polIoWxin0AoamZPX4nfi8oA30oDJk6yVfm3PZgoMCwedNXX0aLPP069weLucy7PlbL0cNRJWa8YrY/Jx/gwel0Ii5Ksr8qpVlWB5ATW4Mo7JNoSQ95RlTN5wPMhkKm8977iYXmq62mEfDXl61YhRuLiYY8CTkfs8t9f6e29QbBV564SeiI9SRV3himQWuWKFiKuFmTyQchUzM3nTr7bHdAVqirzzKIpg/0CMFy84kFtUTJPDb+5Goc8TtRJyDbKj6u+Q8gCbiIe8nFN7UBEyULoJYppc02pToJiTC4vHDpMHuHbczuvESaZn50BV8aTGJZPn4PyMoi1imamV38f4XJaPPPQqD3z6KXK2XCQzqJ4gpYpqrZuq4+7/JB3v/vFfSblTKS8XwHZINUF2catlSK7suvs9LkJely25pihlyeKzJEu8EiCig/SJOU5O2zAB0KFvMmuMV4IWv5eI380cMbmZKdYwR/lFCi5ZLLWc62kGwU7ZvV7WebedlVdIUXI7w+Tt7AkzrvYy23szAC9k484zeSCLmVLWkDuPJILs6486I9nMLxoZeWCTyQONkahhX7XZ5/bINffLxybmK1Ht32JJsmk1IiQQb2i8AktF3kyquJSR5+RMnt4YmTrKke0Jqio8cWpu9Z8xA8P0y2utwRjuBXdgKUYh5quTa15YzBEniSfSJtaqe8+K+8lwZxBVxVD4mEYxA94Q2ULF3trTtUvK0xfHtSJPGq/0KqnXpFQTtoq8dcOS9MCeZDNbqhBWWpBrQsMbZ1Pk5mVwb3ebirzIACAMicj+wShHLyTrnLMsoZghQwC/mZunTy/yWiwy05OO2VgfGIhyaKyXSmTQCPJshFjAQ6mikrMoryppTJ4nYMKQpgE8O9+AR1TwnH8ccvN4yhnOq93GbIAT8HaNAVCalotYqVLlwRcv8b6/eILb/vjb/M9vn+CbL09y9KKNpkAxY2QG2pLJRfvhrt+Fk9+CF/9WYwrK7Svy9MDfJuYrCS0rzxJUFaWUIYvP2qzIFQB/Yog+Mc+pGQfme/WGmE0mD2SRZwSiZ2uiN/JJ8krI+hxKK+jeY7hK6hjqtJmVV0hRdDlw/QA7u6W64If97yfffyMnq/1tYvJ085XnjUP3HOjj6bPzTNkdnSjUM3m2jFdAOmzOHF+ak1sYl48OBaHXwReR7FATJi9sFHkWGtKFFCqCrFkJeDAOufrCKuB1EfG5jSJvOl1gVFyiqni1/YRD8McgOgRTr3DNSAdet+KsZFOfUyyajO/RIYT8fekOm9FAnfHK6ckFYiJLsMO5qI06dO+Ve8MaOa3elLEsfdaLvFLF3tqT0GMUpPnKxEKO6XSBThYh1Iaxow2ArSJvndATkZviqaQ9WVG+WFvkXab7GehofSavnaYrIGnzSJ9R5B0cjJErVTg1bXNzVkiRVs0yedrmq2Umb8qxOcVE2MfffPhm3N276iV/yxDVzBuSFmMUynn5vfqDNuSaACOHKeGmb/bHRnd52tVrT560DOHuUZJqkOL4M3zkoVd5/X/6Fh/+3NO8einFr9+5i89+8EYAzs3Zs2yuuOX1YluaeMMD0P86ePDfwenvyWNtK/LG5OMq5iuWmbxKEaGWN67xChBMDBMVWcYvTV/+xZeDIdeUM3l+j4LLYiEW8XuYqmoNllrJZiFJVgm1Vx7btVt23mtUAL0RHy5FWGfyimkKLp3Js/fZY0EPXWEf3y/t5ak7v0ABb3uYvO69oLjripm7D0jJ5kNHbRo95etn8iI+m7mQXbvk+IA+/704DsLlbDFTC918pQH0f4slJq+QpOoJo6KYW5MTu2D8CWNWS0d31Gc0xmdSBcbEJOWOMWdjJcBw2PR7XFw3EnfWfKWQAk+QdEm1fu3UzGYvZ/LOX5DGUx1dzpmh1aF7r3ysYfOGtaaRZfOVYga8YXLFsj1lzbIYhVJFRVUhWp2H0GtvHg+2irx1Q0+kfojYKrKlMmGhvYeTTJ4Rn9AmJg/kfMGidH074JT5SjFtocjTmLxVYhTqkJlyNJAWgMQOWeQ1kWPGAnKhteqwWdXcNX12izxPgFP+/ezMPAPzssjLh4ea5vtZQTzs46nqbtwTT/Cn3znBwcEYn3zf9fzg39zB//mm3dww1okQcHbWnptXxa0zETZvg4oL3vZRydB8+w9l06AdBgkgc5xc3lWy8nzMWHXt1TZU0nhlYzJ5Skxugh3JysvNg9sPngCZQtkyiweSybtY1q692hiF/CJZEWpPRp6O7j2SlawpLt0uhb6o3xqTV61AMU1e0Zsk9j/7zp4QJ6bTnNc2iW0p8tw+uQmskSXu7AmzvTvEgy/ajGwpJKVcM68b9Nj8TmocNgE5hxsdAFebrsveA3L9Ka5snEXsyDULKcoeC7ObN/6SPGef/Xzd4e6wbxmTN4mScHAez/iL9spGd7XC4e0Jjl5MspC1Metci2IafBF78+B6ILqq0hf1s5AtGSZK05NyfjMcbxeTpzX+a+by+qJ+3IqwHqOgM3nFir37SahLrr8zx43CEyBYmt+Sa25hbbFcX24VuWKFgCjKjd/lFgAzM3kzx8Dla99mFeqy8rZ3ScnSixM25/KKGVJVH36PiVNbj5cwxeQ5XeTtlE5kmZmGT0cDmkOfxQHwaiFNXvUQCtifI7zQeRM7KqdRLzwrDzh8jnSFfBQHb2KXMsGPfv1qPvn+G7jrql7DUt3vcdEX9XPOTpFXTFNSJJse8DiwcRq4Bm78EFQK7TNdAVlQdow2ZfK6wl7mMhY3I9oGL4N/Y7prguFomptzICsvN2/YuWeLFVu2+BG/h4lSYyYvLULtcdbUoXe3l8/lxS3GKGgzRTkhN1FOfPZdPRFOTKUZn8/iVgR9UQcz8mqxjLESQnD3/j4ePzXHvNXrBuTa4Y+SLpQIeFz24x902Zkus10Yb49UU0ffAUCFqZdXPKVHgFiTayYpaUWeqfNk+CYYuBYe/3id63VP1G80xmeSOUbFJO6uneY/1+XQs0/ey+dOc2RHAlWFJ047NJdXSKF6w/bmweNjcr40PbUiK29xVs6YinYYr4CUsnpCdUWeSxEMdARsyzVzRRuFL8h1t2t3XYyCiwqewlaRt4U1RsjnJuR12Z7JM4q8y7F4sMTktWLeMX1MdhOVNm4+YkOwOAHVKm6Xwr7+KC/adNhUC2mSVZNMnpmZvEJabnKcjpXQbbObmK8YTJ5Fox61mDU/F9EEqf4jKEJFfe4LJAkRjTsrg1AUwd0/9S4A+haebfiakc4gZ+1YnxezlFzymnGsoLnjt6XL2467nHm/ZlglRqEzJIu8qpXZVi2rKKf6LBuMrDuikslTUhetO72BZKtOfsdw2ksXyrbOk4jfzXhBMz2qLfLySVKqCXt5KzAkTMtszzsC1pi8gizyMsK562dnT5hUvswzZxfo73A4I68WfQelU2XNPNE9B/qpVFW++bJFyWa5KM0e/DHShbJ90xWQ7pruwNKc9sK59jhr6ljFYdMWk5dPUjRcWE2cJ0LAkV+T6+Hxh43DtUxeceGC3Ps4abqio0eXJL7M1cMx/B4H8/IKaSpeC4VvLWpiFIysPM1hM7ugu3+3SZ6oKI0dNjsDjNtk8jJFe4oJQDZIZk8Y5lKdaK7pW3LNLaw1artSVqCqqhxUJd9ikdchTSEaSDJWYObVJclIuxAblt0yzYhge3fYngQPoJgig9+c8YoZd82Mc0HoddAtoJuYr9iVa1LMkMVv/wYKeEeuJ636UdKXOFftdtRZ08DANZJJPvdYw6dHE0F750oxQ1Fj8hzbYPuj8C+ehlt+w5n3a4b4NimVbdCsSYR9lKuqNcZXY2gyDp0n6wKNyetl3h7Te+JR6WB67fsAyBbLNpk8N9MFF3jDS2y9qkJ+kaQabK9cM6Z13pdn5cUDXFzMUaqYtMbXjCMyqnb9OMCE7+yRm94nz8wx1NEG0xUdhvnKC8ahA4NRBjsCPGTVZVOX+ftipPJlZ+aTFQW6dkomr1KC1IX2OGvq6BiVsQUNHDb1eXB93tAUCimKikUX1n3vkMXu4x8zDvVEfWSLFdKFMp7kGXnQyfgEHboXwdQr+NwurhuN89hJp4q8lDEqYFkWrxtwzZ02WO9LyTzpQhlFN6xpF5MHmpx1mcNmPMiE5Zm8tCHXtK0M6NoFqYv4Kxm6Iz5GfJrPQzvynjcAtoq8dYQMRLde5BXKVVQV/BQvb7oChvTosnN5pZzcRLbLdEVHTVYeyDkMW1krqgrFDGmzEQpun2TzUhcu/9p0m4q8jlFpCtDEfMUwXrGa11PKOsbQDCSi/LgqGY5xh501Dbh9MHjdKkVeiJl0wbAsN41SloKQ14yjUjmnDQAaIT4mN5bZlfKhrrCNrLyamby2Fh3thC9MxROh126MwtOfkvKePT8FQKZgb1Yk6vdQLFdRg11LjaJSFtQKi+1m8oSQG58GMQpVdWWQ8mWhZ5/h3PWjF3nlqtqeeTwdvQfl4zLJpsxDszgqoDcH/VHnmDxYcthMXgC12l65pqJA776GTJ49d80keZdFWa/LIyXwp78HF6Ujand4acwllNbmbttR5PnCck2eOgrAke0JXrmUsifp1VFMUbbCbtaiYxgQMH+G3tiSXPPUdJpOtPM40Gn/szZD9x5IXawz8hvuDDKTLhpxM6ZQI9e0fS/UyYnZEwzFA+wMaezillxzC2uNnojPllxTzwnzqQXwttD9NGbPLjOXN3sCUNtrugJ1WXkgO0G2slZKOYRaJWvWeAVg8FoYf/Lyr9PdzpyeyXO55ea9CZMXtcnkKeUsOXx4HZBBDXQE+FF1HwDn1W4GHAxCr8PoERks3oB5HtGGqi2xNaoq3QGNmbwNVtDUSHWWIxGSm6BZK+YrWn5b2RVsn53/WiA2YC9GYXECjj0I19xvhOdmCmVbtvi65K0cSCzJNTV5+EI10H6jm0YxCnGLMQpFWeQl1QAel8Drtn9P6Yn4jO+oLfEJOsLd8t69jLHSG4ymWU1YKvI04xXb8Qk6unZJmabOwLZTrgnavOJLKxQCmgoyxQAAIABJREFULkUQ9LosG6/khA3W6rr3SZXS4x8HaqKnknk68hOUhVvOiLUDPVcZksQjOyQr9uPTDrB5NRmTlhskbp/cP82fJuJzE/S6uLRY4OR0mk6RouKNtjf4W3fYrLmn6M0ZS+YrxQwVd5ByVXWgyFty2PzVN+zkn+/T9idbRd4W1ho9EXtyzazmpuSt5lqfyYPLxyjoHd92M3m6/ETLADJseC0P78pNXZqA+ZvnyBHZxbycZNNg8trgXJXYCXOnGj7lUgQRn9tGkZejoPgdccFMhLw8KQ4BcFbtbQ+TB/J3Ui3D+adWPDWa0Io8KzEK5QKoVXLocs0NJk2skeosR0Jj8iyZr2jXj54fuFHhig4w5F7glFUm7yefk8yJJtUENNc3+0Ve0d+1JNfUZH7zlQD+djcaunbLWTSNhQMp1wQLgejae6TMzj6vAiGEwea1lckDrZh5oe7QYEcA1QqrCUtyTX0mz7EibzegwunvyP9vJ5MHUspaSC4Fr9cg4ndbzsnTZzctnSuBOLzu5+HFL0Nq0jCsu7iYp6d8gZSvjY6jPVfJArtS4uBgBwGPyxnJZiFtZEzaKmi0rDwhpFGRZPIydIo0SqiNUk1o6LCpN2dM79+qFSjnjBl52xmt8W0ybmT2OG/a18u1Ca05sTWTt4W1Rq2+3Ap0Js+rFlqUa2pM3uXkmjPHQChLZiDtgr9DzqhoTJ4RqGlV110zK+Jzmy3yDgPq5dm89JT8btqhd+/UYhSqjbvJ0YDHck6eu5wzZtDsQghBumMvHyz+X3ylcmt7ZvIAhm8EREPJ5minXCQtzeVpzGAWuWFoq7NhOxDX3EwbMXm6XNNKkacZr7SkCriSER1gQMxZy9ysVuCZz8D2O+oMHTLFsi1bfD1rLO/tXGoUaUzebHkN3Ez1TVlN512/bk0zedp9dqHqbNTGzu41KvL6DspGZnnpGhnQTBosGdFMaRvd2BCpvJNyTW39PfEt4/3bCkPK+tKKpyJ+j3kmr1KCUpaMCOFzW8+Y5PCvyPd68s+NfOGXLyUZE5Nkw20sfLuvgmoJZk/idStcP+ZQXl4hRV5xwLQoPmY0+nqjfi4l85ycTjPgzbTPWVNHx4g0BqrLyrPI5GnrTtHlUCSL2yu/G/1el5kGxbOkZHuNYavIW0fU6sutQC/yPJWcHKy/HFqVa84ck3p0T5s27zqE0Bw2JZPXG/XjcdnJWpGbuix+8xv3wetl96fJDJiB9KSk/dvhOprYAeWc1Lo3QDTgsczkuas5Sopzm6fBeIBHq9dR9QQNUxjH4Y/J7vLZH614Khb0EAt4rDlslrQiT3XYeGWt4AnIUOT5MyueigdlkWdNrim/F6E5v21YRPrpqM5zdtrCjNXxR6ThyvUfqDucLdiNUJA/m/XEpdFUtSojU4CZkjOut6tCV2VMLxV5fo+L7ojPApMn77MLFWeL0z19MkdwJNHmJkPfQagU6wregQ55L7hgpcg7+ahs0MVHSRccMl6BpSJv6iUI90mJXjvRcxUgGpqvSCbPZJFnzG7aPE8SO2D33fDUJ+lwl3ErgqMTi4yKScodbXDW1FHjsAlSsnlsMm09hxRkY6FSMDImbcX3dG6T872FNH0xP5cW85ycytDjSrfXdAXk/qdrVx2T1x324XMr5pk8bd3J4+B63LULZrTRl/S03LO1K9boCsdWkbeOqNWXW4E+4Oqu5p01Xpk+tqRrbjdqsvIcyVoB88YrIAet+w/BucdXf116CkJtcmlK7JCPzeby/G7LxiveSo6yy7kiT5/D648FHA1CX4GRI1KuWVn57x5LBK3N5Bl5cD48LoGnXXbt7URNF7cWHpdCR9DDrA3jFeHb2HJNov0oVHHnZszLVp/+S3l9a4YrAMVylWKlSshWhIJshKRdHVIKmptfYvIqFuTlZtG5TRo7zaw0X7HK5M2XfY5+7vtuHOEv3n89/e2a8dXRIC5AZ/JMF3nlApz5Aey8C1VVnTVe8YWluyS0X6qp/32d21ZIWUFn8kyuPZqMNakG7TO+R34NsrMoL36JrrCPSxcniIgcoh1B6Dq6dkvVjsbUHtmuzeWdspGXtyxj0jaTB7Bwll5Nrnl6NkMHqbWRJi5z2BRCMBQPmFdiaeuOboTmiDqgS8YoUK1IJu81KtWErSJvXaFLD6zO5eW0mTxXJd+axMobkgv9ajN51Yq8ONptuqKjpsgDab5iOWtFz29SA9b0/yNHYOLpOhnPCqQn22fF20JWXtIik+et5ii7nOuQ6/M8bQst1jFyRDJvl55f+VQixFkrM3mawUi6uoFdJFfJykuEvMxmrDF5ZVz4vG3+nbYbEZmV12dWsrl4Ho4/JA1XXEvstN5Mc2Imb8GlNdoy08b8b8rq/coMXB7JNk3Xm68MxgOcN7spKyTB5SNZEo4yeSGfmzv3tmHWeTkSO2U8S02Mgt/joivsNV/wnntMys123EW+VKVSVQn7HFQ26GtCO+MTatF7oDmTZ3asRGsGJKsWlDXLMXaLZGAf/zg9ES+R7DkAAr1tjHnyBOR8l+aweWAwRsjr4rFTM9bfUx8pccKZti5GQUbnFMtVQuVFCLbRWVNHz16pwqqZ8x3uDJpXYhmFr4NMXmKXjOdaHNeKvNem6QpsFXnrih5tiNhykafJNV3lFo1XhFgKRG+G+TPy4mi36YqO2LCUL2kb7+HOgI2slSV5SMBr4dQevknKJRsUFAbSU+0xXQG5OXUHmsYoxGzINX1qnqrbQSavQ2fy1qDIAzjbaC4vyIUFC4542qKSqno33jyejvg2KestrVxQE2Gf5QiFnPATdEputl6Iyqy8PjFvznzlmc9KZ8Hr3ld3OKPdZ+0YaugRKPNokvnMlMF0pAiujWS4a1fDQPQLC3mq1ZWZi01RTIMvotmdb8BzxeWW0sRlcQEDHQEmzDo7n3hUzvuM3UKqIO/NjjF5sGQH325nTR19B2XzqFDfHInakGsuVAO2WHBA7l0O/xpMv8Ib3C8wJmSmYaS/zc3oGodNj0vhhm2dPG6HyXO0yBuTj/NnDPOzAHmp7Gq3XBOWHDZrGkfD8aBluWZWK/IcWZNrHDbJzGwVeVtYH3QEPXhdiuUYBZ3JU8q51uSaIOfyVpvJ0+cUutewyAPp/IZ0aLKctaItTFnVb82tbuSwfGw2l6eq7WXyFEVm/qwSo2CJyauU8VCm2kojoEXoMyxtc9bUEe2Xi1mD38lIIkilqpqfKdIGvZMOG0esKYwYhTMrnuoKe625a5Yy5OzOz1wJ0CRuA655TrYao1Apw08+CzvuXNo8achoDIadjEl94z+jRrQ3nYZ8ElUoZPDbd5RrBd17pMS3RqkwFA9QrFTNzRkVUuALa46jG/Rc6TsoGauauICBWMC8XPPEo3Ld8IXJFOR67NhMHixtVtdCrgnQu18+auyVjrDPgrumHhFScYDJAzjwbgj3cm/m7xhVJqmoAn93G2fyQBZ5syelLBcp2TwxlbY8YmM4gKuaNNEOgx/slHPr86fp0yTOnWis2poWebUOmwGS+bK5ZrTW4Hd0Rl5vjswc25JrrvcHeC1DCGErED1brOCmjKiWWjNegdWZPFWF574oDUjWciYP6gLRwXrWCkgmz1KRF+mTDEmzubzcvHTbaheTB3IubxUmL1OsmGeuNKORltjeFjGakOdbWzOtdIzcLH8ny/KbRrXIDdPmK9p5kix7Nq5cM968yEuEfJaNV7Kqb+MzecEuUDzsDqRaZ/JOPCIbTde9f8VTepEXslGIuRRByOtiuhrV3nQGCkmq3gjgrOyxKbr2gFqpi2nRZdfnzRQ3hRT4ImSLlY3LhPcdlAqS1CXj0GBcFnmq2iKrmbwoTVF23gVAWmO67Bj0rEDPVfKxHYHfjaDPK16qn8uL+D3kS1Vza4/GWs1VHGqmub1w4y+xO/0Edyk/YVLpaW8WHMhCRq0YWYW375GM0ENHJ629nx4/ovrxuhTcdufB49s0uaYskEYD2nUcXIOipmNUyp5rijxLMVhG4Sv/DXbuswaCCbnXvfCsVGe1qzG/AbBV5K0zuiM+pi26NeWKFQJoXdlWmbxAR/OZvO99BI5+Fe787aW4hXZjeSC6dpMwPScCxs0ig40Zl5EjkjVqtNDrIcbtvGEkdki5TGUlk6m7WJpm84q6Nb5zhhqDHQH+6oGbeNe1g469Z1OMHpEbMj0UWD+sFZrnZk3O5WlF3kLFu3GZCJ1tamC+0hnyMp8tUTYtY82SUX32pVXrDUWBSB9j3mTrM3lPfUo2b/bcs+KprCbXtLt5j/g9TJWD0sxBY/LKHsnsrUmxpM9Z10g2Bzu0QHQzTbVCGnxRcqUNzOQ1MV/JFissZFu8v57Uog12yCLPkGs6WeSN3Qr3fwW2v8G591wNHSPgi66QsuozpaYkm4Z7rENMHsB1v0hZ8XFAOcOMZ8CZ91wNepGtFTJ7eiPs7AnzjecuWHs/Y07RIdMiLSuvK+xFEXBVTPv9rAWT53JrDps1MQpxff9m4n6iN+ercn/jyPcihCQqdGfuLbnmFtYL3REfUxaZvFypgh/tZ1su8poweUf/Hr79B3DoPXDLb1r6PJYQHQDEyqy8OQtMXiFFWfFRwWU9XHjkMGRnG0sm01r3rq1M3k4ZAL54bsVT0YBcaJMmZyNUwxrfWdfE1+/san+IMyzN5S2TbPZEpGWz6aw8Ta65UPZsXCYi2Ck3Yw3MV7r0QPSsOcmmWkyTVn1rIx1sNyL9DChznJvLXr7YXRiXTN41760zXNGh55jaLWgifjfJQlV22dNyJs8o8tbiOtLVGTUzNEYguikmLwneMNlieePKnXVZYg1jNdhhMjfw5KNyLeiT+XI6kxdxciZPCMkUrpX9uxDyu1mWlae7w5qSbGoFzWzJZ0+WWItQgouj7wRgwd/m3ECQBh6KG6ZkjIIQgnsP9fPEmTkmrUg2tUb0QtUhWXznNlg4h1uobO8Osz+m3fPXosgDKQGvY/J0JZYZJk9T1lQdjjRK7JJxOLBV5G1h/dAT8VmeycsWK0Rd2k231Q18o5m8C8/CV34Zhm6Et/33tc0TcXkg0m8Ued1hH36PhawVgGKGohLAayd41SgoGkg29RDjdjJ5nXqMwkrJps7kmTVfKebkwqL4NmjIdWKnvEkvK/IURTDSGbQg19QW2tIGlmsK0TRGIaHlb5qNUagW0mQ3A5MHEO0nXpmlVFEv79b7E81w5dpfaPi0Ph9sn8nTzCtC3VKumV+k6JaZhGvCiHlDcga6hskL+9zEAh5zTF4xTdUXIV+qbtzrJ9ABsRHrMQrVCpz8tpzh1NZLvRngKJO3Hug9IIu86lJzxBKTl0+CcDFfdDl6fqeu+SUqqmAx0kZnTR1ur1yTtSIP4N5DA6gq/OMLjfNsV4VuRuPUnGJ8mxwhWTzP3/zyEd62W8tSDK1VkbcXFs4ZhVos4CHsc1uSayYrsjnpdzt0rnTVnB9bM3lbWC/0RPzMZ0sUyyalVUC+VCHu0W66Zpi8/KJcpEDOFXzhPnkRvOev2h+A3ggdw/JGgZ61YsGGF6CYJq8E7W08unZBoLNJkaczee2Ua2qW2Q2KPN2hz2yRV8jKhcXt26Ah10JIhrVBKPqolay8YhYUD8mSsnHlZtA0RiER0pg8k+YrajFLhk3grgkQGSBUmALU1SWblTI88xnJlsRHG75EN9QI2TBegZqssXC3IdcsuNawyAPJ5k2vzMoz1XkvpKh41vhztwN99XEBpoq8i89Cbs6QakJNkeckk7ce6DsgN94LZ4xDepFnKqe1kAJ/lFyp6qg6IDK0nzcX/wvnxn7asfdcFT17jUB0gJ09Yfb2RfjG81aKPHkvmi87NCpQ47AZD3nx5Oelp4IvZv+9W0H3HkA1RimWsvJMyjWFQqrsIuBxoVht0C9HXZG3NZO3hXWCHohuZS4vWyzT4dFuui0br2izdvlFab/+xZ+Tf77vC+s3nLosK89SoCZAIU1eCeD32Dit9YKikcNmehJcXsmGtguhLinDayAXtTqTl9eKPJd/A4dcjxyBhbOQrJ+FGOkMcW4u27pZAshFxRvUjCM28IYsvg3mzy41bDToTJ4px0SAYpqs6ndOWrWeiPbjKmeJkOP41CpF3vGHZRTFdR9o+hInjFdgOZM3DYVF8i55Ta7Zedi9R27IaliawbjJQPRCiqKWubmhGwJ9B2H2uBFDkgh58bkVLiy2oKw58SggYMcdxiGd5dr4TJ6Un9YWwHqDMW1qJi+F6otQrFQdbQYMdAS45tqbuH3fGsg1AXr2ScVETVzN264e4Omz8+ZzFQtJ8ATJlFSCHgfOE8NlWWv2ZWellF9Zo6294bBZM5fXGTQv1/SGyZacPU/qzAO3mLwtrBeMrDwL+u5ssUJMl2uaYfJAzuV99Vfhwk/g3f/bmCtYF8SGpLOdtvGwlLUCUEyREw4EC48cloHkujxTh56R1045qxDSfKVBILpVuWYpJ4s8TyBi//OtF5rM5Y0mguRKFabNZE2W5KKSK5Y3NhMRH5NSnWWFr87kmZVrilKWLD7bjNUVAS0Q/YZEnu8fn27+uqc/BeE+2P2Wpi/Rc/Ls3lcifo+cpw0tMXk5JezIe7eMrt3SbU5zMwbJ5E3Mt+gqWSlBOU9JK043dEOg9wCoVSMuQAhhfBeXxYlHof/qus1julDG4xL43Bt8W9WzFxB1c3l64WrOeCVJ1SvdZJ28z7oUwUd+5moODK4VW7UXUOsKmXsPySzOf3jepAFLTcakI3LN6KDMadRdlrMzazePB9L1VfHUMZ1D8QDjcyZcaksZ8IZkJIuTa098TM5T+mLg9jn3vhsMG/xutPHRE5HySCuB6PlShajbZJGns1AP/y689BV44+/B3rea/rsdRWwYKkXDvXK400LWCsgwZwL2zUCazeW1MyOvFomdDZm8qM7kmcwrKuUlk+HdyEVe3yHJVi8LRR9JWIhRKGZQPcGN7Q4IK7u4GmIBDy5FMJsxd0+RRZ5/45pp1CIqi7y3DFf58am5xveShXNw/BG4trHhio5soUzIa19GJAOlS7IwKKYhv0hGaMXSWso1oc6pdigeIFOstHa/1WaKdAZyQ18/fXpcQL1k87LsTH4Rzj9pRCfoSOfLhH1uxFrOtLcD3pBsNNbMKy7N5JmTa+qy3g1rcAUrHDZBOjsfHIyZl2wWUpppkUNrj+KSjqj6bHZ2bm3iE3S4PHK/ssxhM1eqMNvquEBRL/LKzrCbtZ8tvu01zeLBVpG37jDkmhaKvGyxQsSs8You13z1H+Dq++D1v2H673Ucy2IUhuIWYxQKabI4MNDcfzW4/TD+4/rj6en2Omvq6NwhHf/K9eeE3+PC61ZMF7/lnByK9gU3cJHncsPwDSsKbyMrz8xcXjGL6glRVVkbd9B2Qc/KW2a+oiiCzpDXHJNXLqJUSzInbyNvyHREZaf9pq4C5arKd15dxsqrKnzjN2WH99r3rfpWmWLZEVlixO+mUK5SDuhObyoZEUQI1o796d4jH2vMV0xlk+pFnpDX3YbevHeMgTe8zHzFf/mZvFPfldlpO5YVeYXyxp/H09F7oM55dMld04zxyiIl9yZoBnRul2MaNeYrINm8588vctZMhE8hDb6wsxmTtbPZulxzLbHCYdNkjEIxA55ge3I3d70ZRm929j03GLaKvHVGIuRFCGtMXrZYIaKYzMnTqfyhG+Hej66tk2YzxIbloyYh0rNWTMcoFNOkVb996ZPbB4PXrZzLS0+ujRVvYiegNnROjPo9pmfyytqwty+4QY1XdIzcLDdkNTmPQ/EgijCZlVfMUHHL62VDbz5iQ5pUp7H5yoyZIq8kv79Nw+RFZJE34l6gK+zlkeXhxU/8bxmb8OY/kMZPqyBTqDjiOKpvlLOeuHEsRZCgx7V27E+oSxpL1WzKjKy8VuaLNCe8nKLN5G3kc0VRZFxADZM32BFkKlWgUK40/7mTj4I3AsM31h1O5cuEfc0Z4Q2FvgNyBjqfBMDrVvC5FVIFczN5JbceEbKBzxOXR9rxLyvy3qpJNk2xeYWU8xmTWlYeoBV5ayjXBClnnT9jzCzqMQotj9wU0tr4RBuUNXf/Ibzjfzr7nhsMW0XeOsPtUkiEvExbiFHIlyqEjZm8Fu3xEzvh7f8D7vvi+jhpNoLB5GlFnpWsFYCCQ0UeyLm8i88Z1sBUK1LvvhZMXmK7fGxovuImmTOXk1fVNmaBYNT2R1tXjBwGVBh/wjjkdSsMdATMyTVLGSpufZO6gYu85VKdGnSFfcyZkWtq53kGh/Kb1hueAATiKOmL3LW3l+++Or3kYDz1Mjz8O7LLe8MDl32rbLFsOz4BliRvaXdNkacG1978Z+B1MP6k8b9GVp4JJi/LJmiSgJxFn3xRMrtIJg/gUjPzFVWFE9+C7bevkPimCyUiG910RYcRFr80l2e4w7aKQoriZmDyYIXDJsgG47UjHeaKvGLK+YzJ+DYpIc7ManLNtS7y9sjZVm2/oiuxWjLPe/wTcPYH0LPXOQnrFuqwVeRdAeiO+C0FomeLFUJCZ/JaLPKEkHlQa5Wj0gr8MdkZ1eSaetaKqRgFVYVimqTqc0aCN3JEhpJPPC3/PzMjb2RrMZOnZ+U1MV8xK9dUC1kKqoeg3+vEp1s/DN0gB6kbmK+Yk2tmKLnkJnVDu2tCfRe3Bomwt/WZCJCxEkBO9Tk7/L6eiAxA8iJv2tdLqlDmx6dnoZSHv30A/FF4x8daUjKkC2XbzpqwxOQllSV33qQaIOBd42V47Ba5Yc3MABAPyrxIM3LNjNCvnw1+rvQekI6HC2cBaUIDq7CaM8dh8ZzMx1uGTSfXhDopa9TvlsZBraKQ3ByzmyDn8hbOGREIOu49NMDLF5OcXC2mpRaa46ijGZP6bPbFZ6WMeK1n0HTTvkd/H7JzhH1u4kHP6kosVYVH/wM8+G/gqrfBW/6IXGmDu11fodgq8q4AyEB080VerlRT5LmvEFbOCoSoi1HQs1ZMMXnlPKgVUhW/M0Xe0A2AWJoBMzLy1oDJC3TI4ekm5itWDGmy+Da23TmANwj9r1tR5OkxCi2jmKWkW8Bv5Jk8aJqVZ3omT2N7M/gcKWiuCET7IXWB1+/swu9R+ObRSbkRmXwR3vGnLTdsnHJ905m8BbHkCjhfDThrNtAKRm+Rj2d/CGiukvEAEwstXENakZeqbiImDwzJ5lJWXhMm7+Sj8nGZ6QosGa9sCsSGZPN1mflKyxEK5QJUiptjdhOgWzdfqc+YfOuhfoSAbzzXIptXSFN2WkWiZ+XpDem1ZvK6dsE9fwynvgOfuBXOPb56jEK1Al//l/D9j8B174ef+TR4/GQ0g6stOIutIu8KgCzyzMs1c8UKAVEAd2DtclHahdhQna33cGfQ3Eye1mFbrPqc6YwHOmQ+jl5Q6HEKa1HkgeaweWrF4VjAY9pdE80af81s2tuJkcNyMSstXS+jiSBzmWLr30sxTVHIpsiG33zoUp3sXN3hrrCPdKFMvrTKbFEtSnJBzuKQ3PlKQKQfkhcJeF3cuqubxRcfgsc/Bjd+CHa/ueW3yRSclWsulD3S8AOYrwTW/hwcuEYqP8780Dg02IqrJBhFXlKV18+GnskDzTlRGMVMX0z+u5qar5x4VCot9I11DTYVkyeEzMu7VFvkmZBrarN82c0wuwk1Dpv1ks3eqJ8bxjr5RitRCpkZyM5SDMpZPseLvPNPyce1Nl4BuOlD8MGHpYT5Uz/FB9W/Y2KuwZx8KQ9/8z545tNw27+WvhCK/B4ci5XYQh02eGWwOdAT9TGTLlKpth7orKoq2WKZAEXJcGx0NAlEbzlrRWMiFioOFjMjh+XsSrVSw+StUWB8YkfTQHSzTJ4oZcnjw2XTAv6KwOjNMm7jwk+WDmluXudakWyqKpSyFJRNUuQ1iVEwsvLM2FgDFVfQdlTAFYPooIxlqZR4604v/3fxT8h37II3/b6pt3HKeCVqOBSWDEnVXHkdZiDdXmkacuYHxqGheIv5cHqRV9WLvA1+/ehxAZqTpN/jojvia1zklfLyO2vA4oE0Xtk0M3kgTWmmjhr5tWGfu3V3zZxsOqWFNF7Z8OdJfEyqpZaZrwC87VA/x6fSvHoptfp7nHgUUEkO3Q446OzsDUGoByb0Im+dRnEGroFf/h7sfyfvmP1zfj/176kmawyv8kn4q5+Gl78Od/9nuPN3DLm8qqpkN3qk0RWKrSLvCkBPxE+lqjKfbV1eVaxUpQU8+dbn8a5kdAxLZyhtNmg4Li1157OtszMA82Wvg0XeETkoPfkSZHQmbw2LvPSlFTMAurtmy8Uv4CpnKYgNLOetxfBh+XjuR8YhIyuvlSJv8iWoFFkMjgKbYfPROEYhEZbRLHOtSja162dTNIx0RPsBFVKXuOfkfyRGmi+P/T+tOxFryDhkkrCUNVY2XHpnK+vEnI7dAlMvGQzwYDzAfLZEtniZTfyl5yHQyULVj8cl8Lg2wRZCN1/R0DQr79xjMkh+x8oir1iuUihXN49cE+QaVMpKwzHk+dtykac1bOdd8jzf8M00xSUzJmtcaXXcfaAfRXB5Nu/4QxDqYbFDsoKOspud2+T+CdY2J285/FF49yd5bP/vcb14BfUTt0gZZ3oK/vKt8hp615/D4Q/X/VixUqVSVTc+43sFYhPcoTc+uiNyQ2bGfCVXlDIsv1rYHEWeEaMgFwc9a8WUDS+QIYDfqQVlRC8oHpc3KW+49TxCu0jslI/LzFdiAQ9VVUqDWoWrkqOomNvYXrEIJaBrT10o+mhC/k7ONpKHLMeJRwAY73w9wNrPQzmNuCxWl5uvJMKSyZtp1WFTa65UN8O9REdEBqLz7T/Ed/JBvhD5Rb5wLrb6zyyDVExUHNm86+8hi7weEC4Wiu712QAbc3myWWIYjqzG5lUrcPxh2PVmciV188h6ew/I60eTGA52+BsXeScflXlpY7f3W5mzAAAgAElEQVSseCqj3Y83jVwTJBMOxppsSq6ZnABgRivyNvzsM0jJZgMmrzvi48iOBN94/mLz5mulDCe+aVw74HCDUW/2wfoxeTqEoHDoft5e/AOK3hh85p3w8ddLZdJ9fw2HfmbFj+j72U1zT7mCYKvIE0L8jBDiJSFEVQhx/bLn/p0Q4oQQ4lUhxFtqjt+tHTshhPi3dv7+zYIevcgzMZeX02ZtfGredGf6isSyGAU9oLclG15YMo5Q/fjdDt0oOoYhOiS7T+nJtWPxYMlhc7a+yIsG5CbCjGTTXd5ERR7A6BEZo1CV10DY5yYR8rYm1zzxKPQeZM4l5xY2fIfZG5Jzosvkml0heU9p2XxFk2sK7wbPUqyFFojOc5+H7XeQv/5DvHQhefmw6xoUylqH2QHjFbdLIeh1yY1ybBBC3WRL1fVhkwevlbPcmmTTCERf7bs5/yTk5mH3W5y1gF9v6OYrWlzAQCzAhYXcyg37iUdl48+38hrRm26bismLLS/y3GSKldbGShYnAMGU6MTrUnBvBsa35ypZvOYXVzx176EBTs9keOlCsvHPnn9S/tyuNy0VNI4WeWPy0R24ItQYQ/Egx9RhHr7583DN/ZIJ/YWvwa43Nnx9VvtOQpvF2fkKgt0r70XgXcD3ag8KIfYB7wH2A3cDfyqEcAkhXMDHgHuAfcB92ms3H+ZOw/FHWnppT0RK6cw4bOoXhWfTMHl6kScXFGPT0WqMgmYHnsZhI4ORw7LIS02unekKQKeelbeSyQNMZeV5qjnKrs1U5N0ChcW6ubyRVmIU8kn5u9z1RnKaLG3DF3kgu7hzZ+oOdWpM3my6xXuKFoau+NaIqV4L6ExeoBPe+XHetF8Wfd98eXKVH6qHztA45ThqSN5u+y24/8vrVyy5fTB8g8yooiYQfbX77bEHZYTJjjs3V6bVsriAwXiAfKlaPyqQvCDn0xpINQFDxhjZVEyetiZrrJyR89iKZDM5AeEeMmVlc9xjoanDJsDd+/twK4KvN5NsHn9Iu3buMPZujl4/+mz2WscnNIG+fzuTFDKM/DdflvebJsga6/Emun6uENgq8lRVfVlV1ZVnPLwD+KKqqgVVVU8DJ4Abtf9OqKp6SlXVIvBF7bWbD4/8e/jS+4yQ1dXQE5Vd92kTRZ7eDfJW81dE58Y2Iv0glDppSEfQ07pc85VvUA72cErtd5byHzkMqYtyFmUtmTxvUMpl5pYzebLIM8Pkeav5zVXk7XqjXDBf/ppxaLQzePkYhdPfk9mHO99oLLSbQh7SIEYh5HXhcyumjFcqKHi8m+g8CXZKi+6f/guI9rOjO8z2rhCPHG29yFvqMDtV5HlIFUoQ7oa+gzIva702wWO3SvfE3Dw9ER8el1i9qXbsITmnHOggu5mc8KIDEIgb5isDjaSrJ78lH5uYrmS0TapT58kVgVAXuHzGmmwYBxVaWHuSExAd3FzNgJ698nHq6Iqn4iEvr9/ZxT80k2wef0ReO/4Y2VIbijxdrrkezpoN4Pe46In4lmIULpNHahS+m2E9vsLQLg59EBiv+f/z2rFmx1dACPEhIcRTQoinpqen2/Qx24RyEU5+W3bHc/OXfbnf4yLidzOVNC/XdFdym0Ou6fLIznuNw+ZwPMh4K0xedg6OPcTCjrdTweWcaxUszeUVkmvL5EFDh019oTVT5PnUPFX3JjhHdATisO12OPo1o4kykghxYTFHobxKZMCJR8AbgeGbyBUr+NzK5nAcjW+TTENNrIQQgq6wj5lWmbxiljz+jZ+lWAsh4G3/HXbcYRx6075eHj8123LcRtpg8py5p9SaV5QrVYoVB0ORzWL09YAKZx9DUQT9sVViFObPys3tnnsA2XnfNHmKQkjJ5vNfgj9/Ezc981v8K/dfI37yGWkaMXdaziKGe5dYv2XQ2a1NJdcUQko2lzF5LZmvLE5AbHBz2eLHRsATgqmV5isA9x7q5/x8jmfHF+qfWDwvWeJdMrYl1w7WSpdrrvc8Xg10h/RW0BZ2cwtAC0WeEOKbQogXG/zXVgZOVdU/U1X1elVVr+/u7m7nX+U8xh+XrowgN18toCfiY7rVDRlLF4W7skncNUFKNmsMJIY7WwxEf+nvoFpiats7AYcleD37wBeVf15LJg/kXN6yIs+Qa5rIyvOp+c1lqAGw7+2SvdIkVqOdQVR1FXmvqsqZmu23g8tDbjPZNcfHABUWztUdToS9zLXM5KXJCv/mYiIa4E37eilVVL77amuNQ11G5FTxG/F7SGqb5LZ09M1g8DppC39Gl2wGmGh2vz32kHzcfTewCTOt7vhtOPhucPuIzDzLh11f58DTvwOfeQf8yevg6N/DjjubMhKpwiaUa4JUkyzKIi/capGnqhqTN6TJkTfJeaIo0L1nRVaejjfv78PrUvjG88uC0fWxHa3IawtrFe6R+8ArqMgzk3XcljnFLQAtFHmqqr5RVdUDDf77+1V+bAIYrvn/Ie1Ys+ObC8cfXvpzy0We35K7pmszFXkjh2H8xzVzeUHOz+eoXm7Q+/m/hu6rmAtLOYXf7SBBrbhkphRIR7y1RGKnZIJrgq5jQX0mr8Uir1rBRwl1s5wjOvbeK+W9R+VtaDRxmay86Velqc+uNwFoMqJNsiFbJSvPjPFKVvVt+kX2mpE4iZC35bm8TEE393GSyZPX7rpvbDx+GFqayxuKr8LkHXtQ3o8S0hBqU8nwQK497/gYvP8bKL/xHFdXP8v/OPgVeN/X5fE7fluGNzfBEpPnWatPvDaIDdUweTU5j6shvyiN0GKaXHOjOxjXoonDJsgG7K27unjopUv1Txx/GDpGZIHIUpHn6HUvhMz/vO4Dzr2nTQzHg1xK5ilXqpd97RKTt4nOlSsE7ZJrfg14jxDCJ4TYBuwCngCeBHYJIbYJIbxIc5avrfI+GxPHH5GZKmDcIC+H7ojPlPFKriQXFaWc3TxF3g0fBFR48pMADMcDFMvV1RnOudOyMDz0s+TK8mbi+KZJl2yuh1wT6sxXwl43QpiQa2quiZtibrMWoS4pNzsqbx9GjMJskxgFLTqBndLdK1es4PdsAsc3WDUrr3XjlSwZ1eeYLPFKhUsR3Lm3h2+/MkWphc2Hbrzi1OYjWiPXvCIkSmO3wMXnIbfAYDzAVKpAsbzseymk4cz3DRYP2FwzecsghKCvI8zRXAdsu026A97+W0v34wZIa3NqmypCAf7/9u48PK7zuvP8960q7EBhJbET3EVSEiXRtHZZq7VZlhwnXpK0ozixnTyPM53pySROOj2T6U67pzPpTrrT3eNptaPYcduOFTtOZFvWLlmSbcmiNkoWKe4LQHADiH2p7Z0/3luFwl4gClWFW7/P8/ABcatIvoIuLu6557znuEzecC/EY5mXaybvecJtjEd9dp6s3e46bac9eE133aZGui+MT229iU26kt8td6aywOOROAEDZdl8GA1w9Wdh/Q3Z/TuXobOhgnjC0ju4+Dak5J5WXz04KhDLHaHwC8aYbuA64AfGmCcArLU/Bx4B3gUeBz5vrY1ba2PA7wBPAPuAR7z3+seF425g5lWfcpmGJZRrnh2eyHjItbtBsJjYuH9u4OvWwSX3wmtfgeg4Hd6svAVLNvc+4j7u/DgT0RVqprH1Hje8uDnHjWDnmJUXCJjUQPRMxCbcaAlT4qOuiUnb74fz78G592iqLqWyNMjx+ZqvHHradUfzurj6qgV8VZOb4ThHJu/8aCSja4qdHGHYlvvna7KAO3Y0MzQR49Wjc9+opRtNNl7JWnfNktmZvHw2G0juyzvxMu11FVgLvYMzsnlHnod4BLamJiH5q9x5Dm11FUsatTEyEcMYHzaOqG0Hm4Dh3rQgb5GfPYPJIK/DfxnfDq+q553vzPnyVevqAXj9hNeL4dhLbqD8lqnvnWQViVmkGclq11Gf+azj8UJ44OVTy+2u+V1rbYe1tsxa22ytvSvttS9aazdZay+x1v4w7fhj1tqt3mtfXM6/X5CSpZqX3AvVLZkHeeEyJqKJVG3/YsYjrgzP2IQ/Gq8kXfNbMN4P73yHzuSsvPnquq11pZrrb4LajlQzmqw2XgFouQx+/5ALQnOprgtMcHbzlYpQxpm8iTE3t8dXrfGTtt/nPr77KMYY1jVUzl2uOTnihj6ndcbzVSbCGG+MwsxMXimRWCLVPGQhicgo47asKH7I3rSlibJQgCcz6LI5luqamKVyzbIQE9EEkVgiVY2R17bhHe93HRSPvUh7/TwD0Q887vYlr7sudchXD0nm0F5XQc9A5o3QhidjVJeGCPihkVO6Wm93zVBPqunX0KKZPK95mt8ar4DburHuenjxL1yWbobL2sOUBgO8fsJrvnLwSbfvdf2NqfeMR2P++prMozMZ5GXQV0HlmivHJ/VKBeTgU+6Gq3GTa82cYblmalZehvvyxiNxyvH22/ilXBNcwLZ2B7zy3+moSwZ581wkel5zWa6dnwCYyuT55QIaKnWB5Ryz8hb9QeuZGHWZvMAcA3xXvXCbe7K6b2pf3pyZvGMvuUyEtx8P3Lniq4Cmvmta0yKARm8g+qLNVxJxzIWjnLb1/uquOY/K0hA3bm7i6X1nFs1yprprZq3xylQ2pCDKNUvKoWM3HP8xHXXJyom0IC+RcDeqm293HZCBeMK60Q9+y1qlaaur4PzIZOpnymJGJmL+K9UEV64JMNhNWShASdAsXq452OOqmKpb/NV4BdwDtVu+AMOn4PW/nfVyWSjIpe1hXj/uZfIOPulKftOqrXyX3ZxHa105AZPZrOPxiMuE+2YLRQHRVzSbouNuFley/jrctqRyTYCzw5k9PRyLxgkHfRjkGeNqy0/vpfz0HtbUlM1/kdj7LfeUbMf9QIGUP2Vb4+Y5O2xmmsmbHHddXkMVPgzywP2/P/029B+lq7GKE/1jsxv1HHrKtb6elonw2Q/ahg0uyEtM7adq9Aain1+s+cqpNwhMDPBS4nL/lZvN44M7mum+MM7+08MLvm9sMrv7Z6aaV8QKZ1Zj1w3Q+xYtZZMYA93pZYq9b7o9SGn78cbz3RU0B9q9B4ynM9hPBO5hgK/GJyTVekHeUA/GGGrKS1L7D+c11OPm3gZD/mpwlbThZvezZJ5s3q519eztGSRy5gD0H0l11Uwai8Tz/z2fAyXBAK21FRmVaya/Jn4vYc0HBXnZdOwliI3DVu+bOty+pHJNyHwg+ngkTn2J90TNT0EeuMxceS288v/NP2slHnV18Zfc494LK1eumU+Nm9wPirSMQ7g88yAvOu4yeaFynwZ52z/sPu57lHUNlURiCc6kPyix1mXXN3wAQmWpw2ORuL/Ok/oNEJ90TRI8TdXuv3fR5iuHn8Vi+HHi0qyVJRa627c3YwyLDkYfjcSoKsve/pn05hUThRIsrb8RbILSU6/SXFM+vVzzwBMuK7N5Kgue7bEShSg1ED3DfXkjkz7N5JXXutmiyTEKZaEMMnndEG4nnrBMxnyY8TUGbvnDebN5u9bVu4Zxr3/PHUirIAF375b37/kccfdvi38PjfmtsqaAKMjLpoNPQqgCurz663Cbm5c3MbToH13jlWsuJcirDXk3+n7akwdQWuUa17z7KJdVj84d5B16Gsb6UqWa4IK80qBPBlwn1a937ajTxyhUZN54JeJl8kr9GuTVr4fWK+HdR1NjFI6n78vrOwwDx6ftxwMfNo5IjlHoPzJ1qMpl8voWK9c89AxjjZdzgbD/nrrPY01NGVd21i06SmF0MrtDv9Pb0K9IK/WL0fF+CJTAsRe9MQpp3z8HHncl0VVT87dSTRL8dvOepn2JQd7whE8zeTBrIHpG3TVr2/2d8U1m8176y1nZvF1dde43B5+ApkumBpV7/L6fNZ2blZdBJm+yeL4muaYgL1usdUHexpvdPgdwQR5klM0Ll4coDQUyHqMwHo1TV+LtF/BLd8107/8M2AT3TD5G78Acs1b2fgsqGlIt8QEmown/1XTXdbmPA8dSh5ZSrhmbcCMFSitrsr2ywrHjfujZw8bSQWDGrLwZoxOSfPeDtvlywMDxH6cOpYK8hTJ5E4PQ/Sp9Le7BlC9vyObxwR3N7O0eXLCL4mgkTmUWs5vJTN5QWrlm3ueIlVam9uW1p8/KG+p15ZppXTWhQEY/rLDm2jKMIeMOmyOTMf8NQk8Kt6dm16bPeZyTte5+J9zu74yvMXDzF1xA+8bXpr3UWlvBxrCluf+1qaquNL5q+rWIzvpKzg4vvrfVd9snCojP7ojz6PxBtycmPTUfnqpnX4wxxo1RGMpwT17Ep3vykho2wNa72XXunwgkIpxO/7pMDML+x+CyX0w1AwD818kLpjp6XjieOhSuKGEylsioKUByhEJ5pU8zeQDbHwCg5dRThAKG4/1ps/IOPe32NSYzXUDCj40jqte4eY77vp86VF4SpKYstPCevKMvgo3T2+T2K/oq8F3Ehy5vBeC7b8x/fR7L8l6rcFombzyS7K5ZAOdh1w1w6k021CToHZggnrBTnaLT9uPBCg1zLjBloSBrqssyD/J8ncnrSAvyShbO5I31QWzCdbv2e8Z34y3Qee2ce/M+0XiEELFZ+/HAh1UkC+iozywj7rt5igVEQV62JH8gpn9TLyGTB8lZeZlm8mKEg8lyTR8GeQDXfI7ySD/3BX46fYzCu4+6/UdXfHLa28ejPtzQXJ/M5J1IHQpXJFtZL57NS0y6gKe8Kpz9tRWKps2wdgfB/d+jvb5iqlwzOu72yW6evidiIubTTMS2++DM29NKNhurSxfurnn4GSit5lTNZUD2RgWsBl2NVVyzoYG/33Ny3i6bo5PZvSFL35M3FokTChhKsz0U+WKsvxFsnJ12P7GEdQ3ADjwOtevcAOg040XS7nxaVnMRrvFKyeJvXI1qO2DsPEQnFi/XTA1Cb/d/xje5N2+ObN7N5g2GbQVn6q6a9ceKpfEKuHJNWHxWnjJ5K6cAfrr4xMEn3bDl9FlqNe5JceZBXnnmQV4kTo3fg7yNtxKt38yDoSfpTs/O7P0WNGyC9vdNe/t41GfNNADKalxZ6kBaJi9Z8pVByWYi4r5ulVU+LtcENxj9+E+4rHaSE8kfKMd+7J4qzyrV9GkmIjk3MC2b11hdRt/oAteUw8/C+psYibmvhe++Jov4+O5OjvWN8eqxC3O+PhrJ7p686rQgr6AeSnVeDYEQm0bfAqDn3AU3BH3rXe5mNk2qDM/n54obiL54ZU0iYf3beAWmVSSFy0sWfriYHIRe2+7f62y6jbfMzuZZy8aBn/Bi4nLe6BmZ9Ucm/FhxNI/OBpfJW2yMggt8ffr9k2cK8rJhctgNW57RRYlQKVStzXxWXnhp5ZrVgWS5ps8aryQZg7nmc1wROELs5Kvu2MBJOPaia7gy4+Zjwo9BHrgHB2nlmrVeJm9wfPFZeTYyyqQNUVlevmLLKwg77gcsdwZencrkHXrKG0R7w7S3+nLUBrgN/i07YX9akFdVSt985Zr9R1yJ+ebbGUvOg/N5dmamey5vobosxCN7Ts75+lgknrUZeeDaileUBL1yzQK62Sutgvb30dy/B4Dh/c9BdGxWqSb4+CHJDG4g+viisxRHvaC3xrflmlNBXk15iJHJ2Pxfk1Qmr6M4Mr7JuXlDPfDG/3THTr9N6dhpXrC7poaie6y1RdVJsrmmnJKgWXQg+ngkVlRVJLmkIC8bjjwPieic9ddLnZU3lNZaeyET0bQgz4+NVzyhq36FESrZeuwb7sDbf+8+7vz4rPdOFNKT8Wyq75pWrpkM8jLJ5JnIKOOUFUZJ2EpauwMaNvG+0RcZHI8yMBZx+/HW3zjrIciYn28+tn8YTr4Cw6cBV6457568Q8+4j5tuK5yZbTlWWRrivp2t/GBvb2rwebrRyezffCRL3gquRKnrBsrOvcWmsHWlmiWV7vtnhuS54vcHAm215URiiUW70ybPG/9m8jrcx8EeqstCWOsaEs1psNt1aq1aUzQZXzbeCp3XTGXzvK07Z5tvnBqK7onEE8QT1p8/e+YQCBja6yro7l84kzdaaNdCH/H5nV+OHHwSysKu8cFMS5iVt6Ym81l5Y5E4VQHvfX4t1wQoq+FHlXdyxdBz7sZ177fcBTWtkUaSbzfv1nlBnjfoOpzK5GUQ5EXHmDA+z+KBe6K6437aBvZQxzC9R/e7IfIz9uOBz4c5J+cGetm8xqoyLoxFZg+IBzj8nMsSN2xkLBKjoiRIwE/jRzL0sd2djEfjPLa3d9ZroyvQ2rumPMTwZNTrsldAN3vrb8QkYnyq4wyXDP6E+IZbpjpFpxkrpIYxK6i93v1cXaz5yoi3R823jVdSvQW6p40AmdNQD4RbIRBIXWf9fp5M7c3rdtm8g09C65Vs3LDJDUWPTXUG920VyQI6GyozyOSpXHOlKMhbruSw5U23Tuv0mBJuy7xc05uVl8m+vPFInAoiblBtsHRJS15t3mr9GCHi8P3/Dc7tnzYbL924Xzc0161zjWZG3Eyv2iU0XgnExosjyAPYfj8BG+eDwdeIHnjCHZuxHw98fpO6ZpvrJurty2usLiWesLMfCMSjcPQF2HQ7GMNoJF605TK71tWxaU3VrJLNRMKVVmWzXBOmOhROFFrZVuc1YILcN/l92sx5DtbNzuJBeuOVAlr7Cmirc9fNnkX2Ew37PZNXWgmVjTDYPa1x0JwGe1KZP983XkmXzOb96P+B7ldhy51c5Q1Ff7d3ak5yUX1NPB31lQvuybPWeiONiudrkksK8pbrzDsw3Dt3qSa4IG9iACKjc7+eZiqTt/i+vPFonEoTgZKqWXvT/KaiZSvPxa+A937gSkEu/YU53zcRTfhzT15ymKpXsplswz44tniQF4yNMVksQV7bVSRqO7k78Co1J3/kvm6Nm2a9zddPU41xXTaPvQhj/TRWu2vKrOYr3a9CZBg23Qa4r0mxlBDNZIzhY7s72XP8AofPTTVKmIjFsRaqsnzzUVMe8ubkxQrrHCyrhvZdNPU8C8A/jV4659vGonFKgoaSoL9vHzIdiD7s90weeLPyetKCvPkyed2pPXwFMwcyF5Jz80ZOg03A1rtSQ9HTSzaLZT9rug1NlfSPRqbPr00zGUuQsMX1Ncklf1+lcyGVMZhdFgakdaaaXQo009qwuyFbLJMXiSWIJSzldsK/TVfSdDZU8tW4N5B3611Q2TDn+1x3TR+e0qmB6K75SmnINW/IpFwzFB8nGvD/OQKAMQS2389Nwbdpv/Cqy+LN8QDE909Tt98PiRgceIImbyD6rH15h58FE4QNHwCSZYk+/Xpk4KNXtRMMGL79WnfqWHKvVbaHOYfLSxieiBbmUOQu16ToaOklPHo4MWeDDd9WTMxQW1FCZWlw0Q6bT797htJggI1NVTlaWR7UdniNV5JVJHNk8hIJd5/j3fMU1BzIXNh0G3RcDVVroO0qWmsraK0t5/UTU0FeUTSjmeH+K9oJBQwP//jonK8XS2VAvvjwjjjHDj4FrVdCTfPcr6fq2Rcv2WysKiNg4OzQwkFe8puinEhRBHkd9RX8KLGTk9s/Cx/4/Xnf59ubj7pO93FGh81MyjVDiXGiQf+fIyk77qeUGKV2Yt4HL+N+f5radpW70dr//alM3lxBXsduqHBPmwuuCUiOrQ2Xc+sla/jOa93E4m4PzdikO0+qV6jxSkEORV5/EwAjXbfTMzDO/tPDs97iSqv8f5NqjGsasdCevAujEf7+tZN85Kq21PeaL3mZvPBC5ZqjZ10DulpXrjlaSHMgc8EY+MT/hF//AQTc9/WudfW8kdZhs2ia0aRpqS3n/ivaeGTPyTkfTCe70/q9kVO+FMl33woZ64fun81fqglLGogeDBiaqssWbbyS3NBcZidd62uf62yoxBLgxfX/HNqunPM91lomYgX4ZDwbSiqgunn6rLyKUEaZvNLEBPFiCvI6rmakpIlJG2Jf+dznylTjFZ/+UAkEYNuH4NDTNJS6c2RaueZYP/S8nirVBHfzke29Z6vNx3Z3cnZ4khcOngOmbj5WpPGKl8kruJu9DTfB9f8Lrbf+NgDP7j876y3F1AmvrW7hgehff+U4E9EEn7lpYw5XlQe17TA5SNhr9jZnuebg1CB0oLBGhORKTTOsuST16VXr6ugZGOeMNxprrFia0czwmzdtYCwS55s/OzHrNd8/dM0zBXnLcfhZV3+dUZC3hFl5i+zJSz4NKi2Scs2WcDmhgKF7gQ5Nk7EE1uLPPXngddickcnLYE5eWWKCeMj/50hKIEDwli/wVfMA/+7p43O+xfflmuC6bMYmaDj1AsbMKNc88jxgU0FeLJ6g+8J4qqFPsbpt21oaq0p55FVXsjk6uTKjAmrKS5iIJhieiBbe9SpUBnf+W5rautjZUcsz+87Mest4JE5lkTTpaVsgkzcRjfOVnxzn5q1r2Npck+OV5ZjXTKU64kazjMyVyRvySp1Te/KKuwQcYFdXPTC1L69YSxMvbavl+k2NfOXHx6Z1G4Ui+XmcRwryluPgk67rVPuu+d9TUgEVDZmPUaguW3RPXjITUZKY8Pf4BE8wYGirq+DkAh2akrMFfVmuCXMORM8kk1dmJ0iE/H+OpKu44XMEbv9XvHjwPC8cODfr9fFIDGOgzM9lROuuh4oGgu/9gPrKUvrTM3mHn4XyWmhz163Hf36as8OTPHBle54WWxhKggF+4ap2ntl/hr6RyakyohUo1wTXKKqQb2xu27aWN04OcH5k+s+jsUisOJppAO115fSNRuacXfvom6c4PzLJZ/2exYNUCWbl2GkCZp5yzcGpQeiQLAEvjvNkPpe2hSkNBlL78oqqGc0Mn71pI6eHJvjB29PvhYuxGU0u+fguZ4Ul4m7Y8uY7UvXX81rCrLy1NeWLB3mR4gryADobKjjZP38mz/czeeq73KDZuPvhGi7PLMgrtxPYIjlH0n3qui46Gyr4d4/tI45qRQkAACAASURBVD5jRtyYt3fT+LkrbTAEl9wLBx6nudJM7cmz1s3H23Czew/w8EtH6Wqs5PZta/O44MLwsd2dROOWf3zzVGpP3kqMUEgq5JvgO7Y3Yy08/970ByXFVIbX5nXYnJnNs9by5ZeOsK2lhhs2N+ZjabnlZefMUDfVZaG5yzWHeiBUnmqM5ts98ktQFgpyWXuY1719eUXXjCbNzVvXsHltNV9+8ei0hk7j0ZUpixdHQd7FOvUGjPUtXKqZtJRZeeEy+kYmZ92Ypks++QjFx4uiXBOgo27hWSupZjR+7K4JrlzTxmHYPSwIZ9B4xcZjlJuom3NUZMpCQf7grm3sPz3Md17vnvbaWCE2vFgJ2z8Mk0N8oGTfVJB3/oArq/JKNd84cYHXTwzw6evXF+Ug9Jkuaanhio5a/n7PSUYnV6ZJQk3aPLVCvgm+tC1Mc7hsVslmQe4lXCHzjVH40YFzHDgzwmdv2ujvh0VJNa2A8cYolMydyRvqcQ+0va9HMZ0nC9m1rp63vaHoxVyaGAgYPnPjBn5+aoifHulLHS/mr0ku+PSOOAcqG+HGfzGtecG8wm1LyOSVkbDQNzJ/Ni+ZtQrGJ4qi8Qq4TN75kclUMDfTeDGUa0KqZDNc4X7QLvQwYHLc64xXUhznyEz37Wzlis46/uOT7007byaKJROx8RYoream+MucT5ZrHnYz0JLXrb/58TFqykL80u7OvCyxEH1sdyf7Tw/zsncjku35Z+lBXiHf2BhjuG1bMy8cODdtH01Bjn5YIfNl8r784lGaw2V8+Iq2fCwr94IlUNPijVEIzT1CYbAnlfED9zCtWM6ThezqmhqKnipN9Ot9yiI+clU7jVWlfPnFqXEKyYqJQr4WrmYK8i5Wwwa44/+ad2bbNOF2GDsP0cWHnK+pcYOrFyrZTN6wBmNjRZPJ62xw2aj5mq9MRN1NSME1MsiW+umz8pJNMuYdSguMjbggL1BenEGeMYY/vnc7Z4Ym+euXjqSOj0XixbEnoqQctnyQK0d/zIVh7yb18LPQuBnqu+gdHOext3v5xPs7/T3IeYk+fEUbZaEA39/rZptmu4wonFauWeg3wbdvW8toJM4rR6eevBfk6IcV0lJbjjHQkzYr791TQ7x06DwPXr++eMYDgDdGoTs153GWoZ7UfjxwpYnFcp4sZNe6qeYryVm+xVo1UV4S5FPXdfHs/rMcOuvuT8ZWqIuxOEV0hcqjZIfN4cwHoi80RiGZtQrEiqO7JrhZecC8JZu+b7wS7gATgAHXgjgZ5C3UYXNyzAvySqtXfn0F6uoNDdy5o5kvPX849T1VVE+Yt3+Y6tgFNk++S3RyHI69lMrife2nx0lYy4PXr8/vGgtMbUUJd1/WQiSeoCSY/Tlfq6VcE+CGzU2UhQI8s29qlEKxzMkD14ynuaZ8Wibvyy8dobI0yK9e3ZXHleVBbXsqkzerXDMec/c36Zk8NV4B3IOCNm8oejF978znU9d2URYK8NcvHQOmxkrogcDKUJCXC0uYlbe2xgV5C41RGIvEMSQwsfGiKcXrrHeZvL98+gBf+PZe/u8f7uNLzx/mmz87wQ/f7k21KPbtzXuoFGrapso1vRvFhZqvnOl22avyuuaVX18B+8I925iIJfjPzxwA3BPmQr+5zprNHyRuSrgr+CojB1+C6Bhsup3xSJxv/OwEd+5oSWXJZcrHvfLVlZgduFoar4C7nt6wuYln9p/BWks8YZmIJorn+wdor58ao3B6cILvvXWKj+/upLayyEaO1HbCYA/VZUFGJmcEeSOn3Tip8FT5ajE16FnMVV1uKPp4pLi+d+bSWF3GR3d18A+vd9PnbcHxfbfrPCrsnzB+4Q0HzSTIa6r2gryhhco1Y5TjNVIokkzempoyPnJlG/t6h3nuvbMMjEWJxBOz3ldXUZqH1eVIfdescs2FgrwTP/8pVwKXXHl9LlZXsDatqeZXrl7HN352gl+/3g1lbQkXyQ1aeZi+5uu5u/dV4gfXQ6AE1t/Id9/oYWAsym/cuCHfKyxI121sTDXdyLZpmbxVcBN827a1XnnVCK3e1yTbYyUKWVtdBXu7XXfEr/zkGPGE5TduKMLvm3A7xMZZWzI2u1xzxvgESJbFF895spBd6+r5wd5ejvWNrorv+ZX2mzdu4Js/O8HXXj6eOk+KooFRHijIy4UlDEQvLwlSW1Ey7568E31jfOOVE3TVGIhSNI1XjDH8p09elfrcWvdEeWA8wsBYlIGxKKGgYV2jj7MSdV1w9EcAqafI83XYnIzFMWf2MhhqorauNWdLLFS/e8cWvvtGD3/2+H7Gi6lcExjddC8bTv+I6L5vQuc12NIqHv7xa1zWHub96+vzvbyCFAgY/o/7dsw7CHs5SoIByksCqyYjdvv2tfyrf4Sn953lF9/nHlhWFHgGMpva6sp54p0JhieifOOV49x9WYu/f87MxyvFbDN9DE+UYK2dujGfMQg9kbBFtXdzMbvW1QHw5skBLm0L53k1+bd5bTW3bVvL1356nJu3rqFSe8JXjPKjuVBW7YYPL6HD5lzlmif6xvjkQz9lLBrnv/zSNnewSDJ5MxljqCgN0lpbwfbWMNdtauT96zNogrOa1a1z51BsMtW8Yb5M3osHznNJ4gjRtZfncoUFq6m6jN++eSNPvXuGk/1jxXXzcck9xK2hJDIAm2/jhYPnOXR2hN+4YYOeni7g7staVizTmSzZXA3nYWttBTtawzy7/0yq6VcxZWja6yqIxBN86fnDDE3E+EwxDD+fi5ela7bniXlluympTJ4L8iZiybm1unkH2OENRY8n7Kp4sJMLn7lpA32jEX74zulVcR1crRTk5Uq4fUmz8mZm8o73jaYCvG985lq21Hv/64o0yCtK9V2AhcHutMYrcwd5T7x5hE2BU9Rven8OF1jYfvPGjbSEy4nGbcHvhcqmhqY2fpbY7j7ZdBsPv3SUNTVlfGinMrz5kizZXC03N3dsX8trxy+k5sWtlnVnQ1ut+xn75ZeO8r6u+lS3xKLjZeka4+eBGZ2dh3qg1HuYjWafzZQcig76miRdt7GRHa1hV1mjwHfFKMjLlSXNyiuftifveN8ov/zQy4x7Ad6OtrBroABF03hFcOWaAAPHqSwNEgyYOTN5E9E43ftfJYgl2HZFjhdZuCpKg/zenVsBH4/amEO4IsRXE3dzuO4GDgU38qMD57wOZ8XzNSg0yUzeaikbvm17MwkLj79zGlg9686Gdq+zcySW4LM3FeFevKSqtRAooSHmOq1Om5U32D1tEHry/iX5MFKmRikU0wPGhRhj+OwH3PeTAt+VoyAvV5Y4EP3cyCTWWi+D5wK8rycDPEgL8pTJKxrJWXkXjmOMobaiZM4g7/n3zrIp7s2Fa1WQl+6juzp48LouPrhjbb6XkjPGGN6ouoGHOv49f/OTE5SGAvzKNevyvayiluyOu1qeYO9sr6WpuozH3l6Z2YGFLDkQvauxkg/uaMnzavIoEIBwGzWRM8Acmby08QkvHDwHwLUbG3O6xEK2q8sFecX0gGQx9+1so7W2nLpKHzfMy7PiuVLnW7gdRs5APArBhZ9urakpIxJL8HbPIL/1tdeYmBngAUS9hgAlRbgBvFjVtLruiGkdNodmzisCvre3lztKT2DL6zG1HbNeL2bBgOFfP3BZvpeRc41VZRw+N8I7pwb5hSvbU118JT9qykOUBgOEgqvjOWsgYLht2xoe2eMabBTTk/dweYi7Lm3mgSvbCRbpEOuU2g6qJlw2d9oYhcEeaJ66rj63/yzbW8O01JbneoUFayqTVzzfO4spCQb4+meuIRRYHdfB1Uhf2VwJtwEWhk8v+tY13qy8X/0frzARjfONz84I8GAqk1eqIK9oBIJQ25EaiB4uD83K5I1FYjy77yxXl5/EtF6RKp+R4tZYXcqe4xeYiCb49I3r872cohcuL6FylY0huH371LzNYrpRNcbw3z+1m3sv1x5Wwu2Ujbl7mNRA9NgkjJ51P5twHZ/3HL/ArZesydcqC1JLbTm/uKuDm7bo65Ju45rq4uxWmyPK5OVK+kD0us4F37q2xj39CgUN3/jstWxvnaPlbkTlmkWpvmtqIPoc5ZrP7DtLLDpJ6+QxaLk7DwuUQtRY5cphbtjcyLYWtfDOtwevX88Nm5vyvYwluXFzE6XBAJF4oqjKNSVNbTslo70ESEyVaw67Et5kZ82XDp4nnrDctq14SuIz9R8/ru0TklvK5OVKaiD64h02d3bU8tGr2ucP8ECNV4pV3bpp5ZrDM4K87+89xTXVZwkkItqPJymNXnlmUQ5xLkDbW8N8+Iq2fC9jSarKQly7ye2x0r6iIhVuxyRiNDE4lclLjk/w9uQ9t/8stRUlXNlZl6dFikiSHsflSnombxFVZSH+4hNXLvym1J48ZfKKSl0XjJ6DyNisTN7wRJTn3jvHn2/qh+NA6yLnkBSNO3c0MzIR49ZL9HRdLt6vXL2OofEo1RpeXJy8ksw20ze1Hzz54DrcQSJhef7AOT6wdc2q2W8q4mfL+i40xvy5MWa/MWavMea7xpi6tNf+yBhzyBjznjHmrrTjd3vHDhlj/nA5//6qUhZ2c2Qy7LC5qFQmT7XMRaV+vfs4cMJrvBLFWgvA0/vOEIkluK7Sm1nUUKRDe2WWazY28me/tJNAsTeOkGW5+7IW/vHzN6gBSbHygrwNpQNT5ZqDrhkP4TZ+fmqIc8OT2o8nUiCW+6jlKeAya+1O4ADwRwDGmB3AJ4FLgbuB/9cYEzTGBIH/BtwD7AB+2Xuv/xnjjVHIbCD6oqJjECyFoJ6oFpU6r/X9wHHC5SVE45bxqBs8+723emmvq2DNyH5oudy1vBYREckGb9vJ+tCFqXLNoR43BL2smufeO4sx8IGtCvJECsGy7gKttU9aa5N9dF8Gkv3aHwD+zlo7aa09ChwCrvZ+HbLWHrHWRoC/895bHJYwK29R0XGVahaj1ED0E6lBs4PjUQbHorx48Bz3Xb4Wc/odaNmZx0WKiIjvVNRDSSUdwf60TF4PhN2t33PvnWVnR51GtIgUiGw+6v8N4Ife79uBk2mvdXvH5jteHMLt2cvkRUZVqlmMqtdCqBwuHEsFeUPjMZ74+WmicctHuyYgOqqmKyIikl3GQLidNnN+ak7eUDfUttM/GuHNkwMq1RQpIIsGecaYp40x78zx64G09/wxEAO+nq2FGWM+Z4zZY4zZc+7cuWz9tfkVbnNz8uKzB1gvWXRcQV4xMibVYTNc4Up1B8ejfG/vKdY1VLI1cdS9r1WZPBERybLadtbavundNcPtvHDgHNai5k4iBWTRDV3W2jsWet0Y8+vAfcDtNtkBAnqA9GFwHd4xFjg+8999CHgIYPfu3Xau96w64TawcTc4NLzM9tnRMQV5xaqua1q55rHzo/zkcB+/9YGNmNPPuL2aa7bleZEiIuI74Q6a4m+7IC8yBuP9UNvOc++dpam6lMvba/O9QhHxLLe75t3AHwD3W2vH0l56FPikMabMGLMB2AL8DHgV2GKM2WCMKcU1Z3l0OWtYVVKz8rKwLy86pj15xcobiJ4M8r615yTxhOW+nW3QuxfW7oBgSZ4XKSIivlPbTk28n/HxsdS9TKKmnR8dOMfNW9eqg69IAVlua8b/CpQBTxljAF621v62tfbnxphHgHdxZZyft9bGAYwxvwM8AQSBh621P1/mGlaP1Ky8HmD38v6u6DiUKpNXlOrWwcQAYeNmJb52/AIb11SxvaUaet+CHcXTy0hERHKotoMAlorJczDk7kEOTtYyMBbl1m3ajydSSJYV5FlrNy/w2heBL85x/DHgseX8u6tWNjN5kTGobFz+3yOrj9dhs2Z8qtL5vp1tmMGTMDGgpisiIrIyvPuYpvh5ohcClAAvni4jGIhw02YFeSKFRIO0cqmi3nVGzEaHTe3JK171LsgLDZ2kusw9p/nwzlY4vde9riBPRERWgjcQvdX0Eel3zdK/f9zwvnX11FZqm4BIIVGQl0upgejZ2JOnOXlFKzUrz+3L29ZSw5bmGleqaYLQfGl+1yciIv7kZfLaTR+JC90kKhp5s3eCW1SqKVJwlrsnT5Yq3J6lIE9z8opWRT2U1sDACX7/ro+wNuwNnu3dC01bFfyLiMjKKKsmWhKmNdYHQzEGSpoBjU4QKUQK8nIt3AYnfrr8v0eNV4qXMakOmx+5p33qeO9bsPGWfK1KRESKQKSqldaJPgIjI5yIr6ElXM62lpp8L0tEZlC5Zq6F22CoFxKJi/874jGIR5TJK2beQPSU4TMwclpD0EVEZEXFa9ppM32Ujvby7kg1t25bg9dhXUQKiIK8XAu3QyIKY+cv/u+IeiMJVZZXvOpcJg9r3edquiIiIjlgajvYYE5TEh3mRKyeW1SqKVKQFOTl2rRZeRcp6uajKZNXxOq73L7MsX73ee+b7mPL5flbk4iI+F6ovoNKMwnAWdPEDZub8rwiEZmLgrxcSwV5y2i+Eh11HxXkFa+6de7jwDH3sXcv1G+A8tq8LUlERPyvpKEz9fv61g2pUT4iUlgU5OVaNgaiJzN5arxSvJJjFC54+/J631KppoiIrLhQ3VSQt2XLtjyuREQWoiAv1yqbIFCick1ZnlQm7wSMX3BNWNR0RUREVlqte1idsIb379RcVpFCpSAv1wIBCLcuL5MXSZZrqvFK0SoPu3l5A8fh9NvumDJ5IiKy0ryKpP5AHRub6/K8GBGZjwqp82G5A9GVyROY6rDZ63XWbFGQJyIiKyxUxnhpI8GqNo1OEClgCvLyIdwGp964+D+fGqGgIK+o1XfBmXfdfryaNqhek+8ViYhIEajYcjMVyUZyIlKQFOTlQ7gN9v/AzTi7mKdgmpMn4Pblvfe4O4e0H09ERHLlY3+T7xWIyCK0Jy8fwu0Qm3ANMy5GqrtmVfbWJKtPXRfEJ+H8Ae3HExEREZEUBXn5sNyB6Gq8IgD166d+ryBPRERERDwK8vJhubPykuWaIQV5RS05Kw+gReWaIiIiIuIoyMuH5WbyBnugusWNY5DilRxIW9EAtR35XYuIiIiIFAw1XsmH6mYwwYvP5PUfhsZN2V2TrD4lFe5cWrv94hr4iIiIiIgvKcjLh0AQalph4OTF/fm+w7D1ruyuSVane/8D1LTkexUiIiIiUkAU5OVL40boO7T0PzcxBKNnlckTZ8f9+V6BiIiIiBQYberKl8Yt0HfQzcpbiv4j7mODgjwREREREZlNQV6+NG2BiUEYPb+0P9d/2H1UJk9EREREROagIC9fGre4j30Hl/bn+rxMXv2G7K5HRERERER8QUFevjRtdh/PLzHI6z/s5uyVVmZ/TSIiIiIisuopyMuX2k4Ill1EJu8wNGxcmTWJiIiIiMiqpyAvXwJBt6/u/BI7bGpGnoiIiIiILEBBXj41bl5aJm98AMb61FlTRERERETmpSAvn5q2wIVjEI9m9n511hQRERERkUUoyMunxi2QiLlALxN9mpEnIiIiIiILU5CXT03eGIVMO2z2HwYM1K9fqRWJiIiIiMgqpyAvnxq9MQqZ7svrO+y6cpaUr9yaRERERERkVVOQl08VdVC1ZmmZvEaNTxARERERkfkpyMu3xi0uQ7cYa6HvkPbjiYiIiIjIghTk5VtThmMUxvphYlCdNUVEREREZEHLCvKMMX9qjNlrjHnTGPOkMabNO26MMX9ljDnkvb4r7c88aIw56P16cLn/Aate4xYYPedm4C0kOT5BmTwREREREVnAcjN5f26t3WmtvRL4PvB/esfvAbZ4vz4HfAnAGNMA/AlwDXA18CfGmPplrmF1S3bY7Du08Pv6NCNPREREREQWt6wgz1o7lPZpFWC93z8A/K11XgbqjDGtwF3AU9bafmvtBeAp4O7lrGHVa8xwjEL/YTABqOta+TWJiIiIiMiqFVruX2CM+SLwa8AgcKt3uB04mfa2bu/YfMeLV30XBEKL78vrOwx16yBUmpt1iYiIiIjIqrRoJs8Y87Qx5p05fj0AYK39Y2ttJ/B14HeytTBjzOeMMXuMMXvOnTuXrb+28ARL3HDzTDJ52o8nIiIiIiKLWDSTZ629I8O/6+vAY7g9dz1AZ9prHd6xHuCWGcefn+fffQh4CGD37t12rvf4RuOWhffkWQt9R6DzmtytSUREREREVqXldtfckvbpA8B+7/ePAr/mddm8Fhi01vYCTwB3GmPqvYYrd3rHilvTZleOmYjP/froOYgMK5MnIiIiIiKLWu6evH9vjLkESADHgd/2jj8G3AscAsaATwNYa/uNMX8KvOq9799Ya/uXuYbVr3ELxCdh8KQr3ZxJnTVFRERERCRDywryrLW/OM9xC3x+ntceBh5ezr/rO8kxCucPzR3kpWbkbczZkkREREREZHVa7pw8yYbkGIX5Omz2HXYdODU+QUREREREFqEgrxBUNUF57fwdNvsPuwAvuOyJFyIiIiIi4nMK8gqBMV6HzfkyeUe0H09ERERERDKiIK9QNG1xe/Jmshb6j6izpoiIiIiIZERBXqFo3AzDp2ByZPrx4dMQHVUmT0REREREMqIgr1AkO2zOHIquzpoiIiIiIrIECvIKReM8QZ5m5ImIiIiIyBIoyCsUDRsBM7vDZv9hCJZCbWdeliUiIiIiIquLgrxCUVIOdetmd9jsO+wGpAeCeVmWiIiIiIisLgryCknTljkyeeqsKSIiIiIimVOQV0gat7jMnbXu80TCBXnajyciIiIiIhlSkFdImja7cQlDp9znw6cgNqHOmiIiIiIikjEFeYUk1WHTK9lUZ00REREREVkiBXmFJDkrL7kvLzUjT0GeiIiIiIhkRkFeIalphdLqqVl5fYchVA7h9vyuS0REREREVg0FeYXEGFeamcrkHYH6DRDQ/yYREREREcmMoodC07hl+p487ccTEREREZElUJBXaJq2wMBJiIzChaPqrCkiIiIiIkuiIK/QNG4GLBx9EeIRZfJERERERGRJFOQVmsbN7uOBx91HddYUEREREZElUJBXaFJB3hPe5wryREREREQkcwryCk1ZNdS0wfApKKl0YxVEREREREQypCCvEDV52byGjW6sgoiIiIiISIYU5BWixi3uozprioiIiIjIEinIK0RNXpCn/XgiIiIiIrJECvIKUSqTpyBPRERERESWRkFeIeq6Dnb9Gmy9K98rERERERGRVSaU7wXIHEqr4P7/ku9ViIiIiIjIKqRMnoiIiIiIiI8oyBMREREREfERBXkiIiIiIiI+oiBPRERERETERxTkiYiIiIiI+IiCPBERERERER9RkCciIiIiIuIjWQnyjDG/Z4yxxpgm73NjjPkrY8whY8xeY8yutPc+aIw56P16MBv/voiIiIiIiDjLHoZujOkE7gROpB2+B9ji/boG+BJwjTGmAfgTYDdggdeMMY9aay8sdx0iIiIiIiKSnUzeXwJ/gAvakh4A/tY6LwN1xphW4C7gKWttvxfYPQXcnYU1iIiIiIiICMsM8owxDwA91tq3ZrzUDpxM+7zbOzbfcREREREREcmCRcs1jTFPAy1zvPTHwL/ElWpmnTHmc8DnANatW7cS/4SIiIiIiIjvLBrkWWvvmOu4MeZyYAPwljEGoAN43RhzNdADdKa9vcM71gPcMuP48/P8uw8BDwHs3r3bzvUeERERERERmc5Ym534yRhzDNhtrT1vjPkQ8DvAvbjGK39lrb3aa7zyGpDstvk68D5rbf8if/c54HhWFppdTcD5fC9CfE/nmeSCzjPJBZ1nstJ0jkku5Os867LWrsnkjcvurjmPx3AB3iFgDPg0gLW23xjzp8Cr3vv+zWIBnvfnMvqPyTVjzB5r7e58r0P8TeeZ5ILOM8kFnWey0nSOSS6shvMsa0GetXZ92u8t8Pl53vcw8HC2/l0RERERERGZkpVh6CIiIiIiIlIYFOQtz0P5XoAUBZ1nkgs6zyQXdJ7JStM5JrlQ8OdZ1hqviIiIiIiISP4pkyciIiIiIuIjCvIukjHmbmPMe8aYQ8aYP8z3esQfjDGdxpjnjDHvGmN+boz5Xe94gzHmKWPMQe9jfb7XKqubMSZojHnDGPN97/MNxphXvGvat4wxpfleo6xuxpg6Y8y3jTH7jTH7jDHX6Vom2WaM+Rfez8t3jDHfNMaU63omy2WMedgYc9YY807asTmvX8b5K+9822uM2TX/35w7CvIugjEmCPw34B5gB/DLxpgd+V2V+EQM+D1r7Q7gWuDz3rn1h8Az1totwDPe5yLL8bvAvrTP/wz4S2vtZuAC8Jt5WZX4yX8GHrfWbgOuwJ1vupZJ1hhj2oF/jpvTfBkQBD6JrmeyfF8B7p5xbL7r1z3AFu/X54Av5WiNC1KQd3GuBg5Za49YayPA3wEP5HlN4gPW2l5r7eve74dxN0XtuPPrq97bvgp8JD8rFD8wxnQAHwK+7H1ugNuAb3tv0Tkmy2KMqQU+APw1gLU2Yq0dQNcyyb4QUGGMCQGVQC+6nskyWWtfAGbO8p7v+vUA8LfWeRmoM8a05mal81OQd3HagZNpn3d7x0SyxhizHrgKeAVottb2ei+dBprztCzxh/8E/AGQ8D5vBAastTHvc13TZLk2AOeAv/HKgr9sjKlC1zLJImttD/AfgBO44G4QeA1dz2RlzHf9Ksi4QEGeSAEyxlQD3wH+V2vtUPpr1rXEVVtcuSjGmPuAs9ba1/K9FvG1ELAL+JK19ipglBmlmbqWyXJ5e6IewD1UaAOqmF1iJ5J1q+H6pSDv4vQAnWmfd3jHRJbNGFOCC/C+bq39B+/wmWTq3/t4Nl/rk1XvBuB+Y8wxXKn5bbi9U3VeuRPomibL1w10W2tf8T7/Ni7o07VMsukO4Ki19py1Ngr8A+4ap+uZrIT5rl8FGRcoyLs4rwJbvO5NpbhNvo/meU3iA97eqL8G9llr/yLtpUeBPL4hSgAAAU1JREFUB73fPwj8U67XJv5grf0ja22HtXY97tr1rLX2V4HngF/y3qZzTJbFWnsaOGmMucQ7dDvwLrqWSXadAK41xlR6Pz+T55muZ7IS5rt+PQr8mtdl81pgMK2sM280DP0iGWPuxe1rCQIPW2u/mOcliQ8YY24EXgTeZmq/1L/E7ct7BFgHHAc+bq2duSFYZEmMMbcA/7u19j5jzEZcZq8BeAP4Z9bayXyuT1Y3Y8yVuOY+pcAR4NO4h8u6lknWGGP+NfAJXHfqN4DP4PZD6XomF80Y803gFqAJOAP8CfCPzHH98h4w/FdcqfAY8Glr7Z58rDudgjwREREREREfUbmmiIiIiIiIjyjIExERERER8REFeSIiIiIiIj6iIE9ERERERMRHFOSJiIiIiIj4iII8ERERERERH1GQJyIiIiIi4iMK8kRERERERHzk/wco2b3dGCmJRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(inverted_val_y[:100])\n",
    "plt.plot(inverted_val_y_pred[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the whole process of inverting to orignal scale is same for val_y and val_y_pred (value addition only), so values are going to be close if they are now\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:31:02.518153Z",
     "start_time": "2018-11-24T10:31:02.504533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-22 23:35:00</th>\n",
       "      <td>1977.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-22 23:40:00</th>\n",
       "      <td>1943.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-22 23:45:00</th>\n",
       "      <td>1938.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-22 23:50:00</th>\n",
       "      <td>1908.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-22 23:55:00</th>\n",
       "      <td>1859.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "datetime                    \n",
       "2018-11-22 23:35:00  1977.59\n",
       "2018-11-22 23:40:00  1943.56\n",
       "2018-11-22 23:45:00  1938.66\n",
       "2018-11-22 23:50:00  1908.56\n",
       "2018-11-22 23:55:00  1859.43"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's predict yesterday's load which is not present in monthdata.csv file as of now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:32:15.240966Z",
     "start_time": "2018-11-24T10:32:15.237019Z"
    }
   },
   "source": [
    "we need 288, 1, 20 matrix with last 20 days load for each 288 time stamp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:32:34.192717Z",
     "start_time": "2018-11-24T10:32:34.142227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00:00</th>\n",
       "      <td>2520.40</td>\n",
       "      <td>2491.14</td>\n",
       "      <td>2487.83</td>\n",
       "      <td>2567.64</td>\n",
       "      <td>2286.14</td>\n",
       "      <td>2313.41</td>\n",
       "      <td>2354.25</td>\n",
       "      <td>2368.88</td>\n",
       "      <td>2335.87</td>\n",
       "      <td>2319.43</td>\n",
       "      <td>...</td>\n",
       "      <td>1905.57</td>\n",
       "      <td>1794.49</td>\n",
       "      <td>1837.89</td>\n",
       "      <td>2016.32</td>\n",
       "      <td>1899.06</td>\n",
       "      <td>1807.17</td>\n",
       "      <td>1763.10</td>\n",
       "      <td>1864.32</td>\n",
       "      <td>1869.17</td>\n",
       "      <td>1861.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:05:00</th>\n",
       "      <td>2476.88</td>\n",
       "      <td>2491.30</td>\n",
       "      <td>2457.67</td>\n",
       "      <td>2540.15</td>\n",
       "      <td>2272.77</td>\n",
       "      <td>2285.99</td>\n",
       "      <td>2329.92</td>\n",
       "      <td>2344.27</td>\n",
       "      <td>2325.04</td>\n",
       "      <td>2300.97</td>\n",
       "      <td>...</td>\n",
       "      <td>1886.34</td>\n",
       "      <td>1795.78</td>\n",
       "      <td>1845.33</td>\n",
       "      <td>1987.13</td>\n",
       "      <td>1881.36</td>\n",
       "      <td>1830.02</td>\n",
       "      <td>1749.68</td>\n",
       "      <td>1856.01</td>\n",
       "      <td>1863.78</td>\n",
       "      <td>1813.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:10:00</th>\n",
       "      <td>2479.99</td>\n",
       "      <td>2485.60</td>\n",
       "      <td>2436.98</td>\n",
       "      <td>2529.15</td>\n",
       "      <td>2258.46</td>\n",
       "      <td>2258.82</td>\n",
       "      <td>2344.88</td>\n",
       "      <td>2323.11</td>\n",
       "      <td>2304.88</td>\n",
       "      <td>2298.15</td>\n",
       "      <td>...</td>\n",
       "      <td>1844.60</td>\n",
       "      <td>1756.93</td>\n",
       "      <td>1826.49</td>\n",
       "      <td>1995.60</td>\n",
       "      <td>1863.87</td>\n",
       "      <td>1811.94</td>\n",
       "      <td>1744.79</td>\n",
       "      <td>1830.61</td>\n",
       "      <td>1828.91</td>\n",
       "      <td>1815.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:15:00</th>\n",
       "      <td>2467.38</td>\n",
       "      <td>2460.68</td>\n",
       "      <td>2421.81</td>\n",
       "      <td>2523.26</td>\n",
       "      <td>2259.57</td>\n",
       "      <td>2255.95</td>\n",
       "      <td>2342.85</td>\n",
       "      <td>2316.47</td>\n",
       "      <td>2298.62</td>\n",
       "      <td>2267.14</td>\n",
       "      <td>...</td>\n",
       "      <td>1872.64</td>\n",
       "      <td>1769.04</td>\n",
       "      <td>1808.62</td>\n",
       "      <td>1983.07</td>\n",
       "      <td>1851.78</td>\n",
       "      <td>1752.79</td>\n",
       "      <td>1731.72</td>\n",
       "      <td>1820.61</td>\n",
       "      <td>1841.75</td>\n",
       "      <td>1804.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:20:00</th>\n",
       "      <td>2460.86</td>\n",
       "      <td>2449.49</td>\n",
       "      <td>2423.02</td>\n",
       "      <td>2509.79</td>\n",
       "      <td>2238.66</td>\n",
       "      <td>2243.72</td>\n",
       "      <td>2315.55</td>\n",
       "      <td>2309.83</td>\n",
       "      <td>2296.95</td>\n",
       "      <td>2257.94</td>\n",
       "      <td>...</td>\n",
       "      <td>1820.45</td>\n",
       "      <td>1768.90</td>\n",
       "      <td>1795.87</td>\n",
       "      <td>1967.24</td>\n",
       "      <td>1829.15</td>\n",
       "      <td>1750.78</td>\n",
       "      <td>1712.27</td>\n",
       "      <td>1819.67</td>\n",
       "      <td>1817.94</td>\n",
       "      <td>1795.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0        1        2        3        4        5        6  \\\n",
       "time                                                                      \n",
       "00:00:00  2520.40  2491.14  2487.83  2567.64  2286.14  2313.41  2354.25   \n",
       "00:05:00  2476.88  2491.30  2457.67  2540.15  2272.77  2285.99  2329.92   \n",
       "00:10:00  2479.99  2485.60  2436.98  2529.15  2258.46  2258.82  2344.88   \n",
       "00:15:00  2467.38  2460.68  2421.81  2523.26  2259.57  2255.95  2342.85   \n",
       "00:20:00  2460.86  2449.49  2423.02  2509.79  2238.66  2243.72  2315.55   \n",
       "\n",
       "                7        8        9   ...          20       21       22  \\\n",
       "time                                  ...                                 \n",
       "00:00:00  2368.88  2335.87  2319.43   ...     1905.57  1794.49  1837.89   \n",
       "00:05:00  2344.27  2325.04  2300.97   ...     1886.34  1795.78  1845.33   \n",
       "00:10:00  2323.11  2304.88  2298.15   ...     1844.60  1756.93  1826.49   \n",
       "00:15:00  2316.47  2298.62  2267.14   ...     1872.64  1769.04  1808.62   \n",
       "00:20:00  2309.83  2296.95  2257.94   ...     1820.45  1768.90  1795.87   \n",
       "\n",
       "               23       24       25       26       27       28       29  \n",
       "time                                                                     \n",
       "00:00:00  2016.32  1899.06  1807.17  1763.10  1864.32  1869.17  1861.12  \n",
       "00:05:00  1987.13  1881.36  1830.02  1749.68  1856.01  1863.78  1813.69  \n",
       "00:10:00  1995.60  1863.87  1811.94  1744.79  1830.61  1828.91  1815.72  \n",
       "00:15:00  1983.07  1851.78  1752.79  1731.72  1820.61  1841.75  1804.82  \n",
       "00:20:00  1967.24  1829.15  1750.78  1712.27  1819.67  1817.94  1795.77  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:36:05.150501Z",
     "start_time": "2018-11-24T10:36:05.145351Z"
    }
   },
   "outputs": [],
   "source": [
    "df_last_21 = df.loc[:, '9':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:51:03.238724Z",
     "start_time": "2018-11-24T10:51:03.183525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00:00</th>\n",
       "      <td>2319.43</td>\n",
       "      <td>2380.31</td>\n",
       "      <td>2283.87</td>\n",
       "      <td>2165.11</td>\n",
       "      <td>2140.82</td>\n",
       "      <td>2016.19</td>\n",
       "      <td>1880.24</td>\n",
       "      <td>1673.24</td>\n",
       "      <td>1781.98</td>\n",
       "      <td>1770.85</td>\n",
       "      <td>...</td>\n",
       "      <td>1905.57</td>\n",
       "      <td>1794.49</td>\n",
       "      <td>1837.89</td>\n",
       "      <td>2016.32</td>\n",
       "      <td>1899.06</td>\n",
       "      <td>1807.17</td>\n",
       "      <td>1763.10</td>\n",
       "      <td>1864.32</td>\n",
       "      <td>1869.17</td>\n",
       "      <td>1861.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:05:00</th>\n",
       "      <td>2300.97</td>\n",
       "      <td>2367.30</td>\n",
       "      <td>2220.99</td>\n",
       "      <td>2125.92</td>\n",
       "      <td>2113.14</td>\n",
       "      <td>1998.45</td>\n",
       "      <td>1878.65</td>\n",
       "      <td>1671.83</td>\n",
       "      <td>1759.52</td>\n",
       "      <td>1740.50</td>\n",
       "      <td>...</td>\n",
       "      <td>1886.34</td>\n",
       "      <td>1795.78</td>\n",
       "      <td>1845.33</td>\n",
       "      <td>1987.13</td>\n",
       "      <td>1881.36</td>\n",
       "      <td>1830.02</td>\n",
       "      <td>1749.68</td>\n",
       "      <td>1856.01</td>\n",
       "      <td>1863.78</td>\n",
       "      <td>1813.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:10:00</th>\n",
       "      <td>2298.15</td>\n",
       "      <td>2342.32</td>\n",
       "      <td>2194.09</td>\n",
       "      <td>2113.58</td>\n",
       "      <td>2083.93</td>\n",
       "      <td>1983.69</td>\n",
       "      <td>1866.09</td>\n",
       "      <td>1647.21</td>\n",
       "      <td>1758.92</td>\n",
       "      <td>1734.35</td>\n",
       "      <td>...</td>\n",
       "      <td>1844.60</td>\n",
       "      <td>1756.93</td>\n",
       "      <td>1826.49</td>\n",
       "      <td>1995.60</td>\n",
       "      <td>1863.87</td>\n",
       "      <td>1811.94</td>\n",
       "      <td>1744.79</td>\n",
       "      <td>1830.61</td>\n",
       "      <td>1828.91</td>\n",
       "      <td>1815.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:15:00</th>\n",
       "      <td>2267.14</td>\n",
       "      <td>2318.59</td>\n",
       "      <td>2198.71</td>\n",
       "      <td>2115.86</td>\n",
       "      <td>2081.58</td>\n",
       "      <td>1948.07</td>\n",
       "      <td>1847.40</td>\n",
       "      <td>1620.10</td>\n",
       "      <td>1736.61</td>\n",
       "      <td>1723.62</td>\n",
       "      <td>...</td>\n",
       "      <td>1872.64</td>\n",
       "      <td>1769.04</td>\n",
       "      <td>1808.62</td>\n",
       "      <td>1983.07</td>\n",
       "      <td>1851.78</td>\n",
       "      <td>1752.79</td>\n",
       "      <td>1731.72</td>\n",
       "      <td>1820.61</td>\n",
       "      <td>1841.75</td>\n",
       "      <td>1804.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:20:00</th>\n",
       "      <td>2257.94</td>\n",
       "      <td>2312.49</td>\n",
       "      <td>2202.56</td>\n",
       "      <td>2099.84</td>\n",
       "      <td>2078.93</td>\n",
       "      <td>1939.29</td>\n",
       "      <td>1822.96</td>\n",
       "      <td>1605.38</td>\n",
       "      <td>1725.09</td>\n",
       "      <td>1707.29</td>\n",
       "      <td>...</td>\n",
       "      <td>1820.45</td>\n",
       "      <td>1768.90</td>\n",
       "      <td>1795.87</td>\n",
       "      <td>1967.24</td>\n",
       "      <td>1829.15</td>\n",
       "      <td>1750.78</td>\n",
       "      <td>1712.27</td>\n",
       "      <td>1819.67</td>\n",
       "      <td>1817.94</td>\n",
       "      <td>1795.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                9       10       11       12       13       14       15  \\\n",
       "time                                                                      \n",
       "00:00:00  2319.43  2380.31  2283.87  2165.11  2140.82  2016.19  1880.24   \n",
       "00:05:00  2300.97  2367.30  2220.99  2125.92  2113.14  1998.45  1878.65   \n",
       "00:10:00  2298.15  2342.32  2194.09  2113.58  2083.93  1983.69  1866.09   \n",
       "00:15:00  2267.14  2318.59  2198.71  2115.86  2081.58  1948.07  1847.40   \n",
       "00:20:00  2257.94  2312.49  2202.56  2099.84  2078.93  1939.29  1822.96   \n",
       "\n",
       "               16       17       18   ...          20       21       22  \\\n",
       "time                                  ...                                 \n",
       "00:00:00  1673.24  1781.98  1770.85   ...     1905.57  1794.49  1837.89   \n",
       "00:05:00  1671.83  1759.52  1740.50   ...     1886.34  1795.78  1845.33   \n",
       "00:10:00  1647.21  1758.92  1734.35   ...     1844.60  1756.93  1826.49   \n",
       "00:15:00  1620.10  1736.61  1723.62   ...     1872.64  1769.04  1808.62   \n",
       "00:20:00  1605.38  1725.09  1707.29   ...     1820.45  1768.90  1795.87   \n",
       "\n",
       "               23       24       25       26       27       28       29  \n",
       "time                                                                     \n",
       "00:00:00  2016.32  1899.06  1807.17  1763.10  1864.32  1869.17  1861.12  \n",
       "00:05:00  1987.13  1881.36  1830.02  1749.68  1856.01  1863.78  1813.69  \n",
       "00:10:00  1995.60  1863.87  1811.94  1744.79  1830.61  1828.91  1815.72  \n",
       "00:15:00  1983.07  1851.78  1752.79  1731.72  1820.61  1841.75  1804.82  \n",
       "00:20:00  1967.24  1829.15  1750.78  1712.27  1819.67  1817.94  1795.77  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last_21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:37:09.835680Z",
     "start_time": "2018-11-24T10:37:09.827169Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_df_last_20 = df_last_21.diff(1, axis=1).dropna(axis=1)  #taking last 21 days, differencing and dropping the nan value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:37:10.686794Z",
     "start_time": "2018-11-24T10:37:10.638311Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:00:00</th>\n",
       "      <td>60.88</td>\n",
       "      <td>-96.44</td>\n",
       "      <td>-118.76</td>\n",
       "      <td>-24.29</td>\n",
       "      <td>-124.63</td>\n",
       "      <td>-135.95</td>\n",
       "      <td>-207.00</td>\n",
       "      <td>108.74</td>\n",
       "      <td>-11.13</td>\n",
       "      <td>67.15</td>\n",
       "      <td>67.57</td>\n",
       "      <td>-111.08</td>\n",
       "      <td>43.40</td>\n",
       "      <td>178.43</td>\n",
       "      <td>-117.26</td>\n",
       "      <td>-91.89</td>\n",
       "      <td>-44.07</td>\n",
       "      <td>101.22</td>\n",
       "      <td>4.85</td>\n",
       "      <td>-8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:05:00</th>\n",
       "      <td>66.33</td>\n",
       "      <td>-146.31</td>\n",
       "      <td>-95.07</td>\n",
       "      <td>-12.78</td>\n",
       "      <td>-114.69</td>\n",
       "      <td>-119.80</td>\n",
       "      <td>-206.82</td>\n",
       "      <td>87.69</td>\n",
       "      <td>-19.02</td>\n",
       "      <td>80.24</td>\n",
       "      <td>65.60</td>\n",
       "      <td>-90.56</td>\n",
       "      <td>49.55</td>\n",
       "      <td>141.80</td>\n",
       "      <td>-105.77</td>\n",
       "      <td>-51.34</td>\n",
       "      <td>-80.34</td>\n",
       "      <td>106.33</td>\n",
       "      <td>7.77</td>\n",
       "      <td>-50.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:10:00</th>\n",
       "      <td>44.17</td>\n",
       "      <td>-148.23</td>\n",
       "      <td>-80.51</td>\n",
       "      <td>-29.65</td>\n",
       "      <td>-100.24</td>\n",
       "      <td>-117.60</td>\n",
       "      <td>-218.88</td>\n",
       "      <td>111.71</td>\n",
       "      <td>-24.57</td>\n",
       "      <td>53.06</td>\n",
       "      <td>57.19</td>\n",
       "      <td>-87.67</td>\n",
       "      <td>69.56</td>\n",
       "      <td>169.11</td>\n",
       "      <td>-131.73</td>\n",
       "      <td>-51.93</td>\n",
       "      <td>-67.15</td>\n",
       "      <td>85.82</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>-13.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:15:00</th>\n",
       "      <td>51.45</td>\n",
       "      <td>-119.88</td>\n",
       "      <td>-82.85</td>\n",
       "      <td>-34.28</td>\n",
       "      <td>-133.51</td>\n",
       "      <td>-100.67</td>\n",
       "      <td>-227.30</td>\n",
       "      <td>116.51</td>\n",
       "      <td>-12.99</td>\n",
       "      <td>74.53</td>\n",
       "      <td>74.49</td>\n",
       "      <td>-103.60</td>\n",
       "      <td>39.58</td>\n",
       "      <td>174.45</td>\n",
       "      <td>-131.29</td>\n",
       "      <td>-98.99</td>\n",
       "      <td>-21.07</td>\n",
       "      <td>88.89</td>\n",
       "      <td>21.14</td>\n",
       "      <td>-36.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:20:00</th>\n",
       "      <td>54.55</td>\n",
       "      <td>-109.93</td>\n",
       "      <td>-102.72</td>\n",
       "      <td>-20.91</td>\n",
       "      <td>-139.64</td>\n",
       "      <td>-116.33</td>\n",
       "      <td>-217.58</td>\n",
       "      <td>119.71</td>\n",
       "      <td>-17.80</td>\n",
       "      <td>71.28</td>\n",
       "      <td>41.88</td>\n",
       "      <td>-51.55</td>\n",
       "      <td>26.97</td>\n",
       "      <td>171.37</td>\n",
       "      <td>-138.09</td>\n",
       "      <td>-78.37</td>\n",
       "      <td>-38.51</td>\n",
       "      <td>107.40</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-22.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             10      11      12     13      14      15      16      17     18  \\\n",
       "time                                                                            \n",
       "00:00:00  60.88  -96.44 -118.76 -24.29 -124.63 -135.95 -207.00  108.74 -11.13   \n",
       "00:05:00  66.33 -146.31  -95.07 -12.78 -114.69 -119.80 -206.82   87.69 -19.02   \n",
       "00:10:00  44.17 -148.23  -80.51 -29.65 -100.24 -117.60 -218.88  111.71 -24.57   \n",
       "00:15:00  51.45 -119.88  -82.85 -34.28 -133.51 -100.67 -227.30  116.51 -12.99   \n",
       "00:20:00  54.55 -109.93 -102.72 -20.91 -139.64 -116.33 -217.58  119.71 -17.80   \n",
       "\n",
       "             19     20      21     22      23      24     25     26      27  \\\n",
       "time                                                                          \n",
       "00:00:00  67.15  67.57 -111.08  43.40  178.43 -117.26 -91.89 -44.07  101.22   \n",
       "00:05:00  80.24  65.60  -90.56  49.55  141.80 -105.77 -51.34 -80.34  106.33   \n",
       "00:10:00  53.06  57.19  -87.67  69.56  169.11 -131.73 -51.93 -67.15   85.82   \n",
       "00:15:00  74.53  74.49 -103.60  39.58  174.45 -131.29 -98.99 -21.07   88.89   \n",
       "00:20:00  71.28  41.88  -51.55  26.97  171.37 -138.09 -78.37 -38.51  107.40   \n",
       "\n",
       "             28     29  \n",
       "time                    \n",
       "00:00:00   4.85  -8.05  \n",
       "00:05:00   7.77 -50.09  \n",
       "00:10:00  -1.70 -13.19  \n",
       "00:15:00  21.14 -36.93  \n",
       "00:20:00  -1.73 -22.17  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_df_last_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:37:39.961768Z",
     "start_time": "2018-11-24T10:37:39.955937Z"
    }
   },
   "outputs": [],
   "source": [
    "dt_df_last_20 = scaler.transform(dt_df_last_20)  # df is now a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:37:58.761714Z",
     "start_time": "2018-11-24T10:37:58.757159Z"
    }
   },
   "outputs": [],
   "source": [
    "X = dt_df_last_20.reshape(dt_df_last_20.shape[0], 1, nlags)  # nlags=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:38:29.261005Z",
     "start_time": "2018-11-24T10:38:28.967508Z"
    }
   },
   "outputs": [],
   "source": [
    "Y = model.predict(X, batch_size=1)  # predict for today's values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:38:49.121075Z",
     "start_time": "2018-11-24T10:38:49.116506Z"
    }
   },
   "outputs": [],
   "source": [
    "inv_Y = scaler.inverse_transform(Y)  # invert to detrended values' scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:53:55.033394Z",
     "start_time": "2018-11-24T10:53:55.027335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 1)"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:56:18.226617Z",
     "start_time": "2018-11-24T10:56:18.219057Z"
    }
   },
   "outputs": [],
   "source": [
    "rescaled_Y = [x+y for x, y in zip(inv_Y[:, 0], df.iloc[:, -1])]  # last day's values added to inv_Y to get it to original scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:58:12.808053Z",
     "start_time": "2018-11-24T10:58:12.801847Z"
    }
   },
   "outputs": [],
   "source": [
    "## get today's load\n",
    "\n",
    "def get_load_data(date):\n",
    "    load=[]\n",
    "    url = 'http://www.delhisldc.org/Loaddata.aspx?mode='\n",
    "    print('Scraping ' + date, end=' ')\n",
    "    resp = requests.get(url + date) # send a get request to the url, get response\n",
    "    soup = BeautifulSoup(resp.text, 'lxml') # Yummy HTML soup\n",
    "    table = soup.find('table', {'id':'ContentPlaceHolder3_DGGridAv'}) # get the table from html\n",
    "    trs = table.findAll('tr') # extract all rows of the table\n",
    "    if len(trs[1:])==288: # no need to create csv file, if there's no data\n",
    "        with open('monthdata.csv', 'a') as f:  #'a' makes sure the values are appended at the end of the already existing file\n",
    "            \n",
    "            for tr in trs[1:]:\n",
    "                time, delhi = tr.findChildren('font')[:2]\n",
    "                load.append(delhi.text)\n",
    "    if len(trs[1:]) != 288:\n",
    "        print('Some of the load values are missing..')\n",
    "    else:\n",
    "        print('Done')\n",
    "    return load\n",
    "\n",
    "yesterday = datetime.today() - timedelta(1)\n",
    "yesterday = yesterday.strftime('%d/%m/%Y')\n",
    "load = get_load_data(yesterday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:49:48.691988Z",
     "start_time": "2018-11-24T10:49:48.686811Z"
    }
   },
   "outputs": [],
   "source": [
    "load = [float(x) for x in load]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-11-24T10:56:37.385421Z",
     "start_time": "2018-11-24T10:56:36.761328Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f151c232710>]"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(load)\n",
    "plt.plot(rescaled_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### References:\n",
    "\n",
    "https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/\n",
    "https://machinelearningmastery.com/multi-step-time-series-forecasting-long-short-term-memory-networks-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!jupyter nbconvert --to script delhi.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TF",
   "language": "python",
   "name": "tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
